prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How can you level up in the early levels?

Read the following text and answer briefly.

Context 1: What is this game all about? (short version) Do you like the board game RISK®? Then chances are you’ll like QONQR  Your job is to join a faction and help your faction (team) take over the world  QONQR is an artificial intelligence that appeared on the Internet  We don’t know where it came from, or its purpose, but we know it is powerful  The Legion want to destroy QONQR because they believe it will enslave and exterminate humanity  The Swarm believe QONQR will advance the human race, and we should protect it  The Faceless don’t care, and want to steal the technology for their own uses  Pick a side, recruit your friends, and help your faction capture the towns and cities in which you live, work, and play  What is this game all about? (long version) Right now an invisible war is raging all around you  At stake: the Past, Present, and Future  A rogue Artificial Intelligence has been detected infiltrating the world’s networked infrastructure  Initial hacking of the source code has revealed incredible new technology  It is not certain whether this AI seeks to enhance or destroy humanity  It is only certain that it is here, and that is has a name: QONQR Those who first detected the presence of QONQR on the global networks have argued fiercely over its intentions  They have split into viciously rival Factions, each co-opting the known QONQR technologies for their own ends  Even now the Factions battle over the entire globe seeking to gather resources and prepare for the coming power struggle  Whether you accept it or not, the war is here  Your survival, prosperity, and even glory depend on the choices you make and the skill you demonstrate from this point forward  You will be asked to join one of three Factions  The choice should not be made lightly  Your Faction Alignment will define your place in the war over QONQR  THE LEGION unite under the shared goals of destroying QONQR and saving humanity by crushing the nascent AI before it can mature  They are led by AGENT SUNDAY, a former commander of the NSA's Turing Task Force which has been valiantly stamping out dangerous AIs for years  THE SWARM are convinced that QONQR promises an era of unprecedented technological advancement and human prosperity  Nanobot weaponry expert KIMYO NAGUMO leads this faction in the battle to defend QONQR and assemble its future tech, accelerating humanity's path into the future  THE FACELESS are a loosely organized faction of militant hackers who want QONQR's technology for their own ends, but want to prevent the unavoidable nightmare of human slavery they believe it portends  When they choose to communicate, they do so through an anonymous vigilante who goes by the name PROMETHEUS    What do I do first? Create a base, then launch  Launch nanobots until your fingers hurt  How do I create a base? On iOS there is a Base icon in the menu bar  For Windows Phone, you will find a bases button on the home screen  These take you to the Base Screen where you can see how many bases you have available to you  Once you create a base, be sure harvest often by returning to the list of your bases  You can see how full each base is by checking the fill percentage icon  Bases stop collecting once they are full  What is the point of creating bases and harvesting resources? You need bases to earn money  Bases collect rare elements over time which you can then harvest for your faction in exchange for qredits  Qredits can be used to purchase ordinance (like nanomissiles) and upgrades which will help you capture and hold battle zones more easily  What do you mean, “launch nanobots?” Nanobots are the invisible soldiers generated by your device (which has been transformed into “Scope” by advanced QONQR technology)  Nanobots fight for control of the battle zones around you   From the home screen click “Current Zone”  Once you have selected your zone, you will be able to deploy nanobots there   Initially, you are just a level 1 recruit  You are only going to get a small attack formation with a limited range   Other solders have to practice with rifles before they get tanks; you are no different  Once you prove your mettle, you’ll get access to bigger weapons  Soon you’ll be lobbing missiles hundreds of miles  How do I capture a zone? If you play for the Legion and are launching nanobots into a zone controlled by the Swarm, you will capture the zone for the Legion as soon as you have destroyed enough of the enemy that your nonbots outnumber theirs  If you are the person who causes the zone to change control to the Legion, you will be listed as the Capturer of the Zone  The Person with the most nanobots in the zone is the zone leader  What is my current zone? How do you know? Your current zone is determined by your proximity to the nearest zone center  So, while you might be inside the governmental boundaries of a city, your scope (phone) could tell you your current zone is a different city, if that city’s center is closer  So I just keep deploying? Yes, in the early levels of the game, just keep deploying and harvesting your bases  You will earn XP (experience points) proving your loyalty to your Faction 	You will level up quickly and soon have access to many more options  So all I can do is just attack? You only get assault bots to start  They are the most basic type of nanobot formation  As you level up you will get many more options, including bots that are good at defense, energy attacks, long-range deployments, formations that will buff the shields for all your faction-mates in the zone, and many more  My faction already controls this zone should I still attack? Yes, assault bots won’t attack your friends  You will increase the bot counts for that zone, which will deter opponents  Attack bots can defend your zone; they just aren’t very good at it  As you level up you will unlock defensive formations that are better for deploying if your faction already holds the zone  I’ll never knock out my enemy at this rate! In the first few levels your impact might feel minimal, but every deployment helps you gain experience  It won’t take long to level up if you keep at it  If you are unlucky enough to be in a zone where someone has already built up huge defenses, you may be in for a long fight  But remember, your scope moves with you  Go explore the world and find softer targets  Once you level up, it won’t seem like a toy gun against a battleship  You’ll get your big weapons once you prove yourself  We have already seen operatives brag about taking down 1,000,000 nanobots in just a couple days  Nothing is impossible  How do I attack a different zone? As you level up you will unlock more and stronger formations  Those new formations will have range  While at the early levels you can only attack nearby zones, as you level up your attacks will go 10-20 miles (roughly 15-30 Km) and you will eventually gain access to nanomissiles that can go hundreds of miles  What should I buy in the depot first? The smallest thing to buy is a refresh  We give you some of these as you level up so you can try them out  Refreshes will refill (or partially fill depending on the size of your tank) your bot bar or your energy bar  But after that, it depends on your goals  There is much to choose from  Do you want to be able to deploy more nanobots on every launch? Do you want to boost your offensive or defensive bots? Do you want to be able to launch missiles into towns far away? All of these things are possible  Look through the depot and see what you like  Most of the time you will need to buy an upgrade before you can buy the ordinance  For example, missiles are fairly inexpensive, but you need to buy the MX Rack Missile Launcher before you can launch them  Buy the upgrade first  What is the difference between qredits and cubes in the depot? A qredit (aka: credit, which looks like the child of a Q and € ) is the type of currency you earn in the game by harvesting your bases  Cubes (aka: power cubes) are purchased with real money in the Bank section of the Depot  We want everyone to be able to do everything in the game for free, by earning qredits, but for those who want to move a bit faster, you can purchase cubes to speed things along  Purchasing cubes is how QONQR makes money  We very much appreciate your support  Every purchase you make helps us to keep making improvements in the game  Future enhancements will enable you to earn cubes in game  Why can’t I create another base? Additional bases become available as you level up  At the start, you will get a new base every 5 levels  If you don’t have any more bases available to build, you will need to level up  If you have a base available, but aren’t allowed to use it, it is because you already have a base in that zone  You can only have one base in a zone  Get yourself to another zone, then create your base there  My bases are collecting credits at different rates  Your bases collect resources faster if your faction controls the zone  Do your best to either put your bases in zones you can control by yourself, or find zones with strong players in the same faction and put your bases there to maximize your credit collection  The game says I’m in a town that doesn’t exist  QONQR tracks almost 3 million battle zones of varying strategic value in 250 countries  Sometimes those zones include locations that haven’t existed for over 100 years  That’s pretty cool if you ask us  If you find a zone that looks like a duplicate or is just plain wrong, however, let us know on the forums under “Zone Corrections”  How do I move my current position on the map? You might need to take a car, bus, plane or train depending on which zone you are trying to get to, but if you want to move on the map, you need to move in the real world  QONQR is a location-based game, which means you play where you are  However we don't want to make you move to play, we want you to plan when you move  QONQR goes with you as you move through the daily activities in your life  Where do I find the Strategy Guide? Here: http://community qonqr com/index php?/topic/1191-the-official-qonqr-strategy-guide/ How do I win? That is for you to decide  There is still much to discover  We don’t even know if QONQR is good or evil  Why is it here? What is its purpose? Help your faction further its goals and unlock all the secrets of QONQR!
Congratulations to the Swarm on their overwhelming victory in Atlantis in May 2015 -- taking and retaining all Atlantis zones from beginning to end is hard to argue with -- most convincing -- well done Swarm!
Rumor has it that the Duggers have 20 scopes  Some families really do have a wife and ten kids    Ha Anyways     Multi scoping is generally not encouraged, usually if you mention that you do it on the forums or in groupme your not going to be a very liked person, even within your own faction  What tends to happen is if you start multiscoping then your enemies start doing it, then you get into a war where ever person has 6 phones and nobody is happy  I've seen or heard about situations like this in a lot of different locations 
Great job, faceless! It was actually an exciting Atlantis and I prefer it that way  Way to bring your A game 
You know what we need in this game? Nano-Nukes!!
Attention (insert faction here), We, the (insert faction here) are tired of the way you constantly (circle one)   A  Cube rage us B  Bully us with numbers C  Talk mean to us It hurts our feelings because (circle one)   A  We don't cube B  We don't have as many allies C  We have no sense of humor and/or no backbone Please refrain from participating in the above selected actions for above selected reasons so as the game is enjoyable for (insert faction here)  Regards, (insert name and faction here) There we go   this should streamline the entire complaint process of the forums  Copy and reuse as needed  You're welcome 
Silver, you mentioned in a blog that there are levels beyond 150, is it safe to say there is no level limit anymore or is that for us to discover? Another question, I'm not sure if I'm the only one noticing this but it seems like bot decay doesn't work against Zone Assault bots  I've hit two players I know have been inactive for well over 6 months but my attack had the same effect it did yesterday before the update  However I did notice against Deflection bots I am getting 2x the kill power  Also in response to decontaminatoR  Paid gaming isn't just something for adults  When I was 13 the best options for handheld gaming was GameBoy or GameGear and the games at the time cost anywhere between $29-$39 dollars  I had a paper route to pay for my games  So, no offense, but you can afford $0 99 for a game  You don't even need a paper route, just check under your couch cushions and I'm sure you'll find a few quarters 
I want to start by thanking Faceless  This round of Atlantis, Legion and Faceless were doubled in sized and probably spending by Swarm  I contacted some great players from the other side and put together a nonaggression pact, this pact was one of the most impressive agreements I've seen in the more than two years of playing  Hundreds of people worldwide stuck to this agreement and put past feelings behind us  It was awesome to see both sides stick so closely to each other in fighting against Swarm  I want to really thank everyone who showed honor by standing behind me and the faceless command when we suggested that, the people who really gave it a chance and then most importantly, to all the players who honored it  Faceless, thank you very much! We stood no chance of winning without you! The battle came down to literally one launch in one of three zones  I'd say that with that being said everyone fought incredibly hard, so I want to give Swarm the respect they deserve  You guys really show out and play to win  Good game, 2 against 1 is not easy, no matter how large your crew is  And Legion, we had many late, late nights, many very long days  You guys killed it this month! We didn't take home a trophy, but I would say we all have something to be proud of! The other leaders who helped me coordinate everything were awesome! So many great people kept everything moving forward 24 hours a day for the whole week  Thank you to everyone who gave it your all for the whole week even when we saw that Swarm had 4x our bots at the end of just one day  Many people would have given up, but we held in and **** near won! I've won Atlantis battles with Faceless and with Legion but I will say, nothing was as fun or incredible as this round! You guys are fantastic and I can't wait to do it all again next month! Hopefully we have more Legion and Faceless show up for the next round, but I know that even if we don't, we will figure out a way, just as warriors do  PEW! PEW! PEW!
I must start with an announcement: Camels are not the only animal in the middle east  You have overused it already  It's saddens me that anyone would still find it funny  Get a bit of originality  For anyone not wanting to read all this drivel, skip to the end (hint: Bold stuff)  At what point have I bullied anyone? When was the last aggressive or threatening message you or any of your members received from me? Never  Legion and Swarm (in the UK) have teamed up because Faceless are dominating in London  That makes sense  It's a three sided fight and if one force becomes too powerful, the other two can join forces to try and take them down  But: We are dominating in the London area while your alliance is attacking players outside of that area  Then you expect me to sit back and do nothing  You are specifically using me as an excuse for why you have teamed up but then you're attacking players outside of my reach, sometimes with Europe involved  If that is not reason enough to attack you then I'm not sure what is  You lot cube, multiscope and have multi faction accounts and still complain  I don't complain about anything you do  Play the way you want to, ill do the same  I am never rude to anyone, always polite no matter what the message is, never brag about what I do, can do or have done, never threaten anyone, try and keep in touch with the few swarm or legion who are civilised to me, listen to any message form any side and if I can help in any way, I try my best to  Formed agreements with enemy (that I didn't need to) that ended up slapping me in the face  I have even in the past taken the time to find out who some of the younger players are so I know to avoid them  When I'm in a different country I try and find who the bullies are  Those players who threaten and brag and laugh at others  Those are my targets (if they exist in those areas)  I don't see how anyone can think I'm a bully  Is it your money? Its none of your business how I spend it  Either way your numbers are way off  700 dollars a day? Did you just decide to blindly strike the number pad to come up with that figure? Best part of one of your posts is saying that the devs should worry about my welfare  What concerns you about me spending my money? Maybe I'll self harm due to overspending? I wont have enough to buy food because I bought too many cubes? I don't understand what they are supposed to worry about  I have issues? At what point did you deduce this oh mighty psychologist? Wait a minute are you my bank account manager? What do you know about how much I can or cant afford? Random guy spends money on something he enjoys  The end  Told you last time to give up on all the drama but I guess thank you for the concern  You play to make me spend more? And? What do you think that accomplishes? In fact how do you even make me spend? You don't even attack me  You sulk when I'm in the UK and when I fly out and you find out, you call in Europe to help you take an zone or two  As they say, whatever floats your boat  As for Atlantis: Please try again  I quit Atlantis when we were winning  I have on occasion involved myself after I was asked to help out but on the whole I give it a miss  The last time (months ago) was the last 10 minutes of Atlantis and Legion fought hard  We lost  I was not the only Faceless player to quit Atlantis  Quite a few of us thought it lasted too long and had too many zones to fight over  I hear the duration has been reduced  You can't honestly believe I changed my sleeping pattern for the game  I was in California for a month  I was jet lagged  My sleeping pattern was a bit off when I got back  You're not even accurate about when I deploy  Pay attention  You should know this by now: I play as and when I want, sometimes every 20 minutes and sometimes I do long stretches and sometimes I'm busy and don't deploy for hours  You wont always win, and nor will I  Try and get satisfaction form at least trying to win or putting up a good fight  About the limit on cubing  Please  I suggested that last year  Twice  Unfortunately multiscoping is allowed and so prevalent that it kind of ruins the idea  These are the facts: YOU and YOUR side threatened Faceless members who support London  One of the members threatened is actually London based  What do you expect him to do while his city is under attacked? A few of your members don't know when to keep quiet  They sent messages to us threatening specific players and telling them their zones will be dropped just because they have helped London  Basically if they help London then they get their zones wiped  And you have the cheek to call me a bully? We stood up to your members specifically because they were trying to bully  That is the reason we went strong months ago and took those big zones  How is this me bullying you? This is me answering your threats  I didn't attack those zones "just because i can", it's just because I should  Because of your threats  You can blame your members for the loss of those 2 or 3 big zones  These are the basics: You attack one of our zones  We look at the list of attackers and pick one of the players who deployed the most, find a zone of his or hers and attack it  We don't need to justify our attacks with "because there are Faceless players within 30 miles"  What, every time we attack a zone we need to send some letter explaining why? You attack us or we attack you, for any reason  That's the game  When some of you were cheating and you could not win you complained, when you cube and lose you complain, when you invite all of Europe to attack and win you still complain  I just think you like to make a fuss  Last words (for now): You think I attack zones as a means of getting attention? I get enough of it form your threads  The only attention seeker here is you with your victim attitude and pity us posts 
I've had some very angry emails today from a couple users who are upset their rival achieved the ability to switch factions freely having played for one of the factions for only 1 hour  I've been accused of doing favors, changing the rules, and various other backhanded deals  It appears it comes down reading the rules  You do not have to play for every faction for 60 days in order to earn free switching status  Here is a common scenario many players have used to achieve free switching status and avoiding playing for one faction they despise  1  Start with Swarm 2  Switch to Legion (earn Spy) play for 60 days 3  Switch back to Swarm (earn Double Agent) play for 60 days 4  Switch to Faceless (earn Mercenary) immediately switch back to Swarm or Legion Below is the complete text on the switch nanobots screen  It is the same text that has been there from Day 1 with the exception of the level 100 rules that went into place earlier this year, where you could switch as much as you want before level 100 , but those switches don't count towards the medals  This text has been part of this description since Jan 15, 2013  "Players that earn all three spy awards, may once again switch factions at any time as they could during the training levels 1 through 99 " Prior to Jan 15, the text said this  "Players that earn all three awards may be given the opportunity to switch factions more quickly in future updates (contact support for more information)" I pulled that right out of source control, which includes the entire change history  Here is the complete text from this page  http://portal qonqr    r/SwitchFaction WARNING: Defection has consequences! Self-destruct will be initiated on all your nanobots  Without the self-destruct, you would be required to battle against your former self to regain control of your zones  You will lose the capture and leadership of any zones you currently hold  Lifetime captures will be unaffected  If you are still completing the training levels and have not reached Level 100, you may switch as often as you like to find the faction that suits you best  Once you have reached Level 100 switching factions has rewards, but also has additional consequences beyond the self-destruct of all your nanobots  Defection will usually result in a demotion in rank  This is accomplished through awards with negative rank points  Those awards are: Spy - First switch to an opposing faction (-20 points) Double Agent - Return to a faction from which you had previously defected (-20 points) Mercenary - Become a member of all three factions (-20 points) Other Faction Change Details: You may not switch factions again until at least 60 days have passed since your last faction switch  Defection point penalties are applied only once per award Players that earn all three spy awards, may once again switch factions at any time as they could during the training levels 1 through 99  The decision to switch factions is one that must be made with strong determination  Nanobots cannot be reanimated once destroyed  You will retain your earned experience, level, formations, qredits, cubes, and upgrades  However, as far as your zones go, you will be starting over 
   has got to be one of the funniest moments I've seen in qonqr yet lol 
The **** change operation was a success!
As a relatively new player for faceless in a region dominated by swarm i can understand the OP  However, judging from the numbers i see here on a regular basis i think you are asking a bit much  My idea would be the opposite approach  Why not add a weapon with extremely short range, let's say like 5km that acts like a bomb and make it much stronk? That would add some serious home advantage  Or alternativley make attack formations lose power over range (exclude nanos and plasma)  Maybe something like that would allow newcomers to at least get a foothold in their homezones  It's just an idea, maybe i overlooked something?
This is by design  Some day it is possible (I said someday) we could offer skins for your scope  So we will need a uniform color scheme  You can tell the formation families based on the shape of the box  Trapezoid is attack, diamond is defense, and octagon is support  It will take some time to get comfortable with the change 
Geophysical based game  Anyways, probably not a bad idea but, considering the issues with the three platforms and the development of blue for those platforms, I doubt the resources are available for development on a new platform  Seen the blog? Its Qonqr meets wheel of fortune!
I am happy to announce that today I both completed the training and captured my first zone 
^THIS so now that qonqr has been thoroughly funded, can we have blue now? Or is that not happening still lol 
Atleast I am not legion and there for we can have this intelligent discussion rather than just compete over who has the best words XD ohhhhh someone bring the bill to legion cuz someone just served them extra double order of stir fried SNAPPPPPP If the devs wont make zone dueling for us I hope out there somewhere are those who would empty a zone and challenge one on one to a local battle  I'd like to see the transcript of deployments made / moves made as well that would be neat I think such events would be cool  I suppose if people give up on atlantis as it works now they can schedule their own tournements in empty atlantis zones   have a team clear the zone   put 1 vs 1 or teams vs teams   like fisticuffs challenges   find out what these warriors are really made of!
@Qonqrd everyone you know must face palm every time you make a post  Its embarrassing  Mega cubers or whatever you want to call them are not great for the opposing team surrounding them but are great for the game itself (money) and for the team they are part of  Multiscopers are not not great for the opposing team surrounding them and bring nothing to the game but are great for the team they are part of  Both have a negative impact on enemy teams/players but only one benefits the game itself  Both can make people want to quit out of frustration  And that's not great for the game  @OP unlimited refresh is over powered  Its frustrating to fight against a ridiculous amount of refreshes  Unfortunately i dont see anything changing unless this game gets a lot more people playing  More people might mean more money for the company from various sources  More money from various players might mean they can limit the players who spend a ton and still generate a healthy income  The main issue i see with limiting refreshes is someone multiscoping and spending money  He now has two, three, four accounts to refresh with and gets the advantage  Its tricky 
ey dun new ho to yet it uff 
Yet you complain almost everyday here, on your website, Twitter, and YouTube channel that the game needs to change because cubing has such an impact 
It was fun  Swarm had me scared at first, but it turned into kind of a bullying match between us and legion  Last hour became p obvious which way it was gonna go  Legion rly stepped up their game in the end there, respect 
We are investigating this  Here is what we know: Several of the accounts used the same password  Most of the accounts belonged to people who knew each other personally  The accounts were all switched from the same IP Addresses  The person who logged in, got into each account on the first attempt, so they knew the password for each account  What you should know: QONQR never stores passwords, not even in the logs  Passwords are hashed (one way encrypted) and can never be decrypted When you authenticate to our servers, we hash the password you gave us and compare it to the encrypted password in the database to see if they match  Access to our database in the could is restricted tightly and we are confident no one breached the system  What you should do: Don't use the same password as other people you play with  Don't share your password with anyone 
I heard all the French players fled to the UK after one German player accidentally shot a single missile into France 
Most factions now use GroupMe or Line as their means of communication, the forums are too slow as a means of communication and insecure for specific faction conversations  Think of the forums are more of a gaming information resource rather than a means of communication  Contact the top players of your faction in the leader boards of your state and they will likely point you in the right direction to chatting with your local faction  The developers are also building some sort of new chat system into the game, we don't know much about it but apparently beta testing for the chat will be happening very soon (next couple of weeks) according to their timelines 
A way to honor the dead? Nah, how bout a way to dishonor the dead 
Could just build it up and retain their capture  Remember Bizzy, staq to the heavens 
I just read this entire thread  I am now tuckered 
Does anyone know if Bot Booster has an effect on Seekers and how much dmg they do to attacking players? Also on the topic of seekers, does the amount of skeers in a battlefield have any effect on how much damage they do?
Interactive map of real-time zone captures 
You know that is something I didn't factor in there  Time  The player who can consistently and constantly launch wins against the guy who casually picks up the phone on occasion or has to work away from a cell phone for 8 hours  Good point  And yes I can't argue skill doesn't factor in, it just seems like less of a factor than other games is all 
I cant see why a closed forum, open only to registered Qonqr accounts, cant be used  **** spammers!
Gotta love synclock! I suspect linjin has a problem  Maybe the dvs should look into it 
No, Naamah   I clearly understood what you were trying to convey  I'll even go as far as to agree that what your facing now is, while fully allowed and deemed completely acceptable by the developers, unbalanced and wrong  However  the imbalance isn't in the game and isn't something that, from a business standpoint, is likely to be regulated  The game is fair  the advantages are provided to all players  It's the players themselves that throw the balance into chaos because, as you've said, not everyone can afford spending several thousand bucks a year in only one game  Truthfully, in my opinion, the moment you admitted to buying cubes yourself your complaint became silly  because I'm sure there's a player out there, who has bought NO cubes, who can make this same complaint about you that you are making about others here  I know these things because I've read them so   many   times in this forum  There was likely a time, ages ago when I was a young Massune, that I even posted a few myself  That was the purpose of my post  I was poking fun at addition of yet another cuber/bully/trash talk complaint on the forum  It wasn't directed at your personal plight so much as the idea that someone, yet again, finds it necessary to lobby for a spending cap on the only real way for this game to make money  As to your specific problem  you, like all those that have raised this topic before you, have few options to rectify the issue  Here are a few that seem to have worked for others  fight harder, recruit better, spend less time complaining and more time organizing, budget for more cubes or quit  I'd rather not see you opt for the latter  but to each their own 
I agree, we should find a way to honor the dead, but I don't think keeping their towers infinitely is necessarily the solution  The game must go on  I'm pretty sure the point of bot decay was to clear the game of inactive player bots so that new players can have a chance to rise up, not to dishonor the bots of dead players 
The following are frequently asked questions about the new server update (so far) It still says Training Complete on my iPhone  -	Download the update from iTunes It still says Training Complete on my Android  -	Sorry, Android will not be updated again until the QONQR Blue beta is released My XP per launch keeps going down -	This is XP throttling and is intended to limit the ability for people to leve1 from 1 to 100 in a single day through heavy cubing  The XP throttle was introduced with the original version of QONQR in 2012,and the throttle formula is the same for levels above 100  The throttle resets at midnight UTC every day  How do I buy the Bot Regeneration Accelerator? -	Currently the Bot Regen Accelerator can only be purchased through http://portal qonqr com  Go to the Depot and review your scope upgrades  The new QONQR Blue clients will allow for this purchase to be made in the app using your mobile billing  I don’t have a PayPal account -	For users interested in purchasing the Regen upgrade, but who do not have a PayPal account, PayPal does give you the option to checkout using your billing information without creating an account  PayPal is not allowed in my country, or I don’t have a credit or debit card -	Please contact support@QONQR com for alternate options Is Bot Regen Accelerator counted as part of the 100% scope upgrades? -	Yes, but there is a bug that does not increase scope upgrade percent when you purchase this upgrade, that will be fixed in the coming days  For all other questions, please read the 7 blog posts prior to 7/29/2015 for information on what was included in the update today 
Bye Fack, its been a pleasure being allied and against you 
The two big issues that are both killing the game slowly and keeping it from growing exponentially are cube injustice and new player ramp  The game obviously also needs to provide a consistent and growing revenue stream as well  I think Silver needs to rethink how revenue is generated if he is going to address cube injustice and new player ramp  For revenue generation I would suggest a model that doesn't give a significant combat advantages  Download and play for free from level 0-99 Pay small monthly fee to get full functionality or play for free at 50% of offensive/defensive funtionality Still buy cubes, but cubes are used for following: - Credit Boost: harvest more credits for a period of time - Range Extension: ability to use standard attack/defense formations at extended ranges - Base Share: get 100% credit attainment even in bases owned by another faction - Purchase additional ordinance - Zone Name Change - ability to customize zone names  "Breggland" - Faction Change with Bots - pay for the ability to keep up to 50% of your bots with faction change - Experience Boost: %increase in experienced gained while leveling - Other: anything that helps grow a player or provides enjoyment, but doesn't tip the battle capability of a scope  New Player Ramp/Integration into Game - Offer paid immediate ramp package: one price to become 100 with full upgrades - Like the changes in Blue - Create new zones in Metro areas that only 0-99 level can launch into, with statewide ranges Understand that catering to those who have money and like to use it for an advantage is a good business model and for those people it might be ok to offer very expensive options: - Shield generators: temporary energy shield that adds X% increase to defense or stops X% of damage - EMP's: turns Absorbs off for X minutes  Does not destroy, just turns off - Chain Lighting: Does damage across multiple players in a zone From a development standpoint I have no idea what is possible, easy or hard, but the general idea is to make the playing field more fair for the standard player while maintaining and growing a business revenue stream 
You need to come to the Northeast US  We handle our business like no other 



ground_truth: Keep deploying and harvesting your bases to earn experience points and level up quickly.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What are the benefits of using binary variables in the SLAS formulation?

Read the following text and answer briefly.

Context 2: Paper Info

Title: SLAS: Speed and Lane Advisory System for Highway Navigation
Publish Date: Unkown
Author List: Faizan Tariq, David Isele, John Baras, Sangjae Bae

Figure

Fig  1 Motivational Example With a slow moving vehicle ahead, the ego vehicle (in blue) may decide to either change lane to the fast moving lane (left) to minimize travel time or adjust its speed without changing lanes to preserve safety but it would be unwise for it to switch to the slow moving lane (right) as that would not benefit travel time or safety 
Fig  3  Simulation Setup Scenario Runner sets up the scenario for the CARLA Simulator, which then communicates with the SLAS and the Planning and Control ROS (Robot Operating System) nodes through the ROS bridge node 
Fig  4  Testing scenario with three lanes: lane 0 (left), lane 1 (center) and lane 2 (right) The expected motion of the ego vehicle, over the course of the simulation, is shown with numbered frames The right most lane (lane 3) is reserved for merging traffic so it is not utilized in our simulation 
Fig  5  Left: Travel time comparison Center: Lane choice (lateral position) comparison The center lines of lanes 0 (left), 1 (center) and 2 (right) have fixed lateral displacements of 0m, 3 5m and 7m respectively Right: Headway comparison With no leading vehicle, the headway is restricted by the visibility range of 50m 

abstract

This paper proposes a hierarchical autonomous vehicle navigation architecture, composed of a high-level speed and lane advisory system (SLAS) coupled with low-level trajectory generation and trajectory following modules  Specifically, we target a multi-lane highway driving scenario where an autonomous ego vehicle navigates in traffic 
We propose a novel receding horizon mixed-integer optimization based method for SLAS with the objective to minimize travel time while accounting for passenger comfort  We further incorporate various modifications in the proposed approach to improve the overall computational efficiency and achieve real-time performance 
We demonstrate the efficacy of the proposed approach in contrast to the existing methods, when applied in conjunction with state-of-the-art trajectory generation and trajectory following frameworks, in a CARLA simulation environment 

INTRODUCTION

Lane changing is considered to be one of the most risky driving behaviors since it is highly contingent upon multimodal trajectory predictions of neighboring vehicles and requires timely decision making   It is further influenced by a number of uncertainty factors such as road conditions, measurement accuracy, and a long tail of behavioral uncertainty of on-road agents 
However, if executed efficiently, lane changing coupled with speed adjustment can yield significant improvement in minimizing overall travel time while ensuring passenger comfort   To elaborate further, consider the scenario presented in Fig    Based on the predicted motion (shown in a lighter shade) of the neighboring vehicles (shown in orange), the ego vehicle (shown in blue) may decide to either change lane left in an attempt to minimize its travel time or slow down in the current lane to maintain safety 
However, it would be imprudent for the ego vehicle to risk changing lane right and consequently get stuck behind a slow moving vehicle even though there is presently a greater headway  This simple scenario highlights the importance of foresight and long planning-horizon in strategic decision making for autonomous vehicles 
Existing methods like MOBIL give us the ability to change lanes but behave greedily (prioritizing immediate rewards) oftentimes, which can lead to sub-optimal performance  It was shown in that the lane changing performance can be improved with an A inspired approach, but the formulation was limited to constant speed 
Such an approach is unable to assess the benefits of speed adjustment 1 University of Maryland, College Park, MD, USA  Email: {mftariq,baras}@umd edu  2 Honda Research Institute, San Jose, CA, USA  Email: {disele,sbae}@honda-ri com  Research supported by Honda Research Institute, USA   in minimizing overall travel time 
As will become apparent in Section IV, it may be necessary at times to sacrifice on shortterm benefits to gain long-term performance improvements  In such a scenario, an approach with speed adjustment coupled with long planning horizon has the foresight to deliver significantly better results  Moreover, the inclusion of speed adjustment in the decision making process inhibits the risk of incurring trajectory infeasibility as the environment conditions may prevent the ego vehicle from traveling at a constant reference speed and the low-level planner may be unable to handle such a discrepancy 
Therefore, in this work, we propose a low complexity receding horizon optimization based approach that outputs the lane change maneuvers coupled with speed adjustments for long planning horizons (> 15s) while guaranteeing safety  The long horizon strategic decision making gives ego vehicle the ability to proactively anticipate and handle challenging driving situations 
Literature review: In the literature, speed and lane changing decisions are generally considered from a motion planner's perspective , which allows for a simultaneous determination of target lanes and waypoints to perform the maneuver  The motion planning methods present in the literature can broadly be categorized into sampling-based, learning-based and optimization-based approaches 
In regards to the sampling-based approaches, single-query methods, in particular the different variants of RRT, are preferred over multi-query methods, like roadmap-based methods, due to the faster execution time and their ability to incorporate non-holonomic constraints   Even though these methods are able to incorporate safety guarantees by sampling feasible trajectories from a reachable safe set , the overall driving experience is often rather uncomfortable due to the concatenation of individual trajectories 
Moreover, the asymptotic optimality guarantees availed by arXiv:2303 00861v1 [cs RO] 1 Mar 2023 these methods do not help with real-world implementation in complex driving scenarios since they tend to have high sample complexity   In terms of the learning-based methods, the preferred approach seems to be the variations of Reinforcement Learning techniques applied in a simulated environment , , , ,  
These approaches, although seeming to work well in simulation, have concerns regarding real-world implementation due to the large amount of training data that they require, the exploration of unsafe behaviors during training, and a general inability to handle edge cases  They mainly utilize neural networks as function approximators which yields low computational complexity but also results in a lack of explainability and safety guarantees 
Lastly, the optimization-based approaches, especially the derivatives of optimal control methods, are abundant in the literature  In contrast to the potential-field based approaches that yield decent collision avoidance performance but are unable to accommodate vehicle dynamics, the optimal control methods , especially the derivatives of Model Predictive Control (MPC) approach , , , yield excellent collision avoidance performance while accommodating vehicle dynamics 
However, this performance comes at a cost of high computational complexity, arising mainly from the non-convex collision avoidance and the non-linear dynamics constraints  This, in turn, restricts the planning horizon to merely a few seconds  The key requirements for the algorithmic design of an autonomous vehicle include real-time operation, safety guarantees, optimality with respect to some metric(s), and accounting for the behavior variability of on-road agents 
Considering these requirements, we propose an optimization-based behavioral planning framework that enables autonomous vehicle maneuvering on multilane highways  While having the benefits of optimizationbased approaches, our method achieves a low computational complexity by employing a binary representation of the decoupled lane indicator dynamics in lieu of lateral dynamics, and utilizing algorithmic modifications to aid numerical computations 
Specifically, our method provides: • optimality with respect to travel time and comfort; • safety and feasibility guarantees; • real-time applicability for a long planning horizon; and • modularity in design, which enables the integration of external trajectory prediction modules  The proposed method fills in the research gap by meeting all the key algorithmic requirements while simultaneously gaining the foresight to make strategic decisions that yield long-term performance benefits, as verified in Section IV 
In this section, we present the algorithmic pipeline and formalize the road, observation and vehicle dynamics models that will be utilized in the subsequent sections 

Algorithmic Pipeline

Fig  illustrates the algorithmic pipeline of the proposed navigation architecture, in reference to the various existing   Algorithmic pipeline of the proposed navigation architecture  The raw sensory input data is processed by the Perception, and Simultaneous Localization and Mapping (SLAM) modules to place the autonomous vehicle relative to the various environmental entities in a unified frame of reference 
This information is then passed on to the navigation stack, composed of the behavioral planning, motion planning, and vehicle control modules  The output of the navigation module is passed down further in terms of actuation commands (brake, throttle and steering) to the actuators  algorithmic modules deployed on an autonomous vehicle 
The taxonomy of the various components of the navigation stack (highlighted by the dotted rectangle) is borrowed from   This pipeline essentially improves the pipeline introduced in by adding a speed advisory system  Our main focus is the development of the behavior planning module, highlighted as SLAS in Fig    SLAS outputs the target lane and reference speed which are utilized by the motion planning module to generate a reference trajectory for the ego vehicle 
The vehicle controllers compute the throttle and steering commands to track the trajectory accordingly  For the motion planning module, we adopt the Neural Networks integrated Model Predictive Control (NNMPC) due to its ability to accommodate the behaviors of neighboring vehicles in the trajectory generation process 
In our approach, we assume that the perception (of other vehicles) and the localization (of ego vehicle) are known without any uncertainty, for simplicity, but the modular architecture avails us the ability to integrate any perception or SLAM module in the overall framework  Throughout the manuscript, Z will denote the set of integers and R the set of real numbers 
For some a, c ∈ Z and a < c, we will write For some e, g ∈ R and e < g, we will write

Road Model

The physical road structure is modeled as a continuous multi-lane highway with negligible curvature and unidirectional traffic flow  The lanes on the highway are clearly demarcated and at any given time k, the number of available lanes for the vehicles to travel on is denoted by N l (k) while the road speed limit is denoted by V l  
Therefore, the set of lanes available for traveling at a given time instant k is denoted by   We work with the Frenet coordinate system where the distance along the road is denoted by the longitudinal displacement (s) and the distance perpendicular to the road is defined by the lateral displacement (d) 
Each lane is assigned a lane indicator variable l  The leftmost lane, with respect to the direction of traffic flow, is assigned a value of l = 0 while each subsequent lane is assigned an increasing integer value for l, as depicted in Fig   

Vehicle Model

Since we aim to have real-time computations for a long planning horizon (> 15s), we model the vehicle dynamics with a linearized decoupled dynamical system  For the highway driving scenario, where the road curvature is typically small, it is reasonable to assume a decoupling between the lateral and the longitudinal dynamics , especially for the behavior planning layer 
Therefore, we utilize a linear constant acceleration model for the longitudinal dynamics and abstract out the lateral dynamics with a lane indicator variable  For the lane change dynamics, we use a moving average filter coupled with a rounding function to model the time required by the ego vehicle to change lanes 
This is compactly represented as: where s 0 (k), v 0 (k), l 0 (k) and L(k) denote the ego vehicle's longitudinal displacement, speed, lane indicator and target lane, respectively, at time instant k; the subscript i indexes the vehicles on the road with 0 being reserved for the ego vehicle; T s denotes the discretization time step; and N corresponds to the number of time steps required to change lane 
The state (x 0 (k)) and control input (u 0 (k)) to the system at time instant k are defined as: where V m denotes the maximum speed of the ego vehicle 

Observation Model

For practical considerations, we restrict the ego vehicle's visibility range to the sensory perception limit, denoted by R v   Then, the set of vehicles in ego vehicle's visibility range at time instant k, represented by O(k), is defined as: where s i (k) corresponds to the longitudinal displacement of the observed vehicle 
Remark 1: For the multi-lane highway driving scenario, occlusion does not play a prominent role so we do not account for it in the existing formulation  However, the proposed framework can easily accommodate occlusion and measurement uncertainties since the receding horizon approach bases its decision on the most up-to-date information available at any given time, as demonstrated in  
In this section, we describe the prediction model to generate the predicted future trajectories of observed vehicles and present a discussion on the proposed receding horizon optimization-based behavioral planning module 

Trajectory Prediction

Reliable behavior and trajectory prediction of other traffic participants is crucial for safe maneuvering of autonomous vehicles  The algorithm proposed in Section III-B is able to incorporate any generic prediction module available in the literature as long as it can provide a deterministic predicted future trajectory for a given vehicle 
In this work, we formulate a low-complexity prediction model that highlights the flexibility and efficiency of our proposed approach  For an observed vehicle i ∈ O(k), the future speed profile is predicted using a piece-wise linear function while the lane profile is assumed to stay constant for the duration of the prediction horizon 
At a given time step k, the estimated acceleration (ā k i ) and the estimated speed (v k i ) parameters are obtained through linear regression with mean-squared error on the past o k i > 1 speed observations  Based on the estimated parameters, we predict the future speed and longitudinal displacement as follows:
Here, H a corresponds to the acceleration horizon while vk i (j) and ŝk i (j) respectively represent the predicted speed and longitudinal displacement for vehicle i, j time steps into the future starting from the current time instant k  Remark 2: Due to the modular nature of the proposed framework, the behavior planning module detailed in Section III-B can work with advanced maneuver-based (e g 
Markov Chain ) and interaction-based (e g  Social Generative Adversarial Networks ) trajectory prediction modules, allowing for interactive maneuvering behaviors 

Speed and Lane Advisory System

The goal of our behavior planning module, Speed and Lane Advisory System or in short, SLAS, is to determine a sequence of speed and lane change commands that would enable the ego vehicle to maximize its speed, thus minimizing the travel time, while accounting for driver comfort and abiding by its dynamical, actuator, and safety limits 
The output of this module is a relatively smooth speed and lane change profile which is then passed on to a motion planner  It is necessary to incorporate the dynamical and actuator limits in the behavioral planning module so as not to provide the motion planner with goals that are not reachable, and jeopardize the safety of the overall system as a result 
In the subsequent discussion, we provide a formulation of the optimization problem for SLAS; highlight the modifications necessary to improve the computational complexity; and, present safety and feasibility analysis  1) Optimization Problem with Integer Constraints: SLAS is posed as an optimization problem, with the objective to maximize speed while minimizing frequent lane changes and abrupt changes in speed 
The output of SLAS, at time instant k, is the control input u 0 (k + 1), as defined in   The optimization problem is formulated as follows: Objective Function: In the formulation above, the optimization variables are the ego vehicle's speed (v k (j)) and target lane (L k (j)), j step into the future, starting from time instant k 
Here, H corresponds to the planning horizon  The scalarization parameters γ 1 , γ 2 and γ 3 in the objective function account for a relative tradeoff between maximizing speed, minimizing lane changes and minimizing abrupt changes in speed respectively  Increasing γ 1 yields a more aggressive behavior with the priority placed on maximizing speed while γ 2 and γ 3 combine to place an emphasis on maximizing passenger comfort by reducing lane and speed changes respectively 
Dynamical Constraints: These constraints are put in place to ensure the dynamical feasibility of the solution  The constraints ( ), and serve to initialize the longitudinal displacement, speed and target lane respectively for the optimizer, based on the values observed at time instant k  The constraints and ( ) bound the ego vehicle's speed by the speed limit and the acceleration limits of the vehicle respectively 
The ego vehicle's speed is then used to calculate the projected longitudinal displacement in   The target lane values at any planning step (j) are restricted to the set of reachable values by ( ), ( ) and   Here, restricts the target lane to the set of available lanes (L(k)), ensures that the lane change, if needed, is made to the adjacent lane only and (17) models the time steps (N ) required for a lane change 
The flooring function can easily be transformed into a couple of linear constraints by the introduction of an auxiliary integer variable, as shown in the Appendix  Finally, l k (j) is merely the internal representation of the lane the ego vehicle is projected to travel on at planning step j  Safety Constraint: The safety constraint ensures that the ego vehicle maintains a minimum safe distance (L s i (j)) to the nearest vehicle i, in its projected lane of travel (l k (j)), at planning instant j 
We borrow the definition of this safe distance from , where the authors provide a formalization, based on the clause from Vienna Convention on Road Traffic that states that "A vehicle [   ] shall keep at a sufficient distance [   ] to avoid collision if the vehicle in front should suddenly slow down or stop "
Furthermore, the absolute value constraint can be decomposed into linear constraints by the application of big-M method and the introduction of an auxiliary variable, as shown in the Appendix  Remark 3: The proposed formulation can accommodate arbitrary number of lanes at any given time instant k  This means that if at any given time, the number of available lanes for traveling either increases or decreases, the proposed formulation will still continue to hold 
This is an important consideration since many a times on highways, some lanes are blocked due to various unanticipated situations such as road accidents, roadwork, narrowing of road etc  2) Computational Complexity Reduction: This section details the optimization problem reformulation with binary variables, optimization warm start technique and lazy constraint implementation, all of which combine to improve the computational complexity of our SLAS module 
Binary Variables: The proposed formulation in Section III-B has relatively high computation complexity (computation time of ∼ 2s in the worst case scenario -slow moving traffic blocking all the lanes) due to the integer decision variables yielding a mixed-integer optimization problem   To circumvent the computational overload, we reformulate the problem with binary variables that replace the integer variables, as follows:
where the Lk (i, j) represents the modified target lane variable, indexed by the lane (i) as well as the planning step (j) and Lk (a, b) = 1 represents the choice of lane a ∈ L as the target lane at planning step b ∈ Z   Then, some of the constraints from the SLAS formulation in Section III-B are modified as follows:
Here, initializes the target lane, (21) restricts the target lane at any planning step to the set of available lanes, restricts the lane change between consecutive planning steps to the adjacent lanes, and ( ) represents the augmented safety constraint  The implication ( =⇒ ) in ( ) can easily be transformed into a linear constraint (see Appendix) 
The augmented minimum safety distance ( Ls i (j)) incorporates the time required to execute the lane change maneuver (N ) from ( ) into the following unified safety constraint: where L l is the width of the lanes (see Fig  ), δ(k) is the signed lateral deviation of the ego vehicle from the previous target lane's boundary at time step k, and γ d (δ k (j)) is the dynamic cost of deviation from the previous target lane (L(k − 1)) 
Moreover, in the cost function , we take L k (0) = L(k − 1)  These costs are introduced to prevent the swerving (canceling of lane switch before completion) behavior, unless absolutely necessary (for safety purposes)  Remark 4: Since the ego vehicle is considered to have changed lane once it crosses a lane boundary, the deviation δ k (j) is considered from the lane boundary instead of the center of the target lane to maintain the continuity of γ d (δ k (j)) with respect to the lateral displacement of the ego vehicle 
Specifically, δ k (j) > 0 if the ego vehicle has crossed the previous target lane boundary and 0 otherwise  This is an important consideration since a discontinuity in γ d (δ k (j)), upon completion of lane change, may lead to infeasibility  Remark 5: The swerving behavior is suppressed but not completely eliminated with a hard constraint since such a behavior is necessary at times to react to the environment's unpredictability 
This reactive strategy, which is a distinctive feature of our approach, avails the algorithm the ability to proactively 'change its mind' in case something unanticipated happens in the environment that can jeopardize safety  Optimization Warm Start: To aid the optimizer in finding an initially feasible solution, we provide the solution from the previous time step as a reference 
Formally, This doesn't imply that the solution from time step k − 1 will hold exactly at time step k, owing to the unmodeled disturbances, but providing this reference aids the optimizer in finding an initially feasible solution in the vicinity of the reference solution  This observation is rooted in the premise that the solution for the long planning horizon is not expected to change significantly between time steps, given the sampling time is not too large, and the predicted behavior of on-road agents does not alter significantly 
It is also worth pointing out that the priority here is quickly finding a feasible solution that obeys the safety constraints and actuator limits, and recursively improving it rather than excessively iterating to reach at a global optimum  In our experiments, it was observed that a suboptimal solution was qualitatively not significantly different from the optimal one 
Therefore, we utilize the cutting planes method for optimization , which first looks for a feasible solution, using our provided reference, and then recursively updates it until either the globally minimal solution is found or the time limit is reached  Lazy Constraints: To further enhance the computational efficiency, we introduce a lazy implementation of the lane changing constraints  
It was observed in our experiments that a feasible solution without the lane changing constraints ( ) can be found several order of magnitude (∼ 10×) quicker than if we include these constraints so we decided to have a lazy implementation for them  With a lazy implementation , the solver finds a set of feasible solutions without the inclusion of these constraints and then determines the feasibility of those solutions from the reduced problem with respect to the lazy constraints 
3) Feasibility: By an argument similar to the one presented in , it is a relatively straightforward proof for recursive feasibility of the problem, i e  the optimization problem will continue to stay feasible, if initially feasible, with the trivial solution being matching the speed of the leading vehicle and not changing lanes 
In this section, we detail our experimental setup, demonstrate the performance of SLAS, and report a qualitative as well as a quantitative comparative analysis  The baselines in our comparative analysis are set to: Extended-Astar (EA ) , MOBIL , and no lane-change model (No-Change) 

Experimental Setup

The implementation setup, depicted in Fig  , is composed of the CARLA simulator (Version 0 9 11) , SLAS module (Section III-B), and the planner and controller module   To solve the optimization problem for SLAS, we use Gurobi Optimizer (Version 9 1 1)   The simulations are performed on a computer equipped with an Intel Xeon(R) CPU E5-2643 v4 @ 3 40GHz × 12 and NVIDIA Titan XP, running Ubuntu 20 04 LTS 
On average, the time required for each optimization step is ∼ 0 096s, while the maximum time limit for the optimizer is set to 0 2s, indicating the strong potential for real-time applicability 

Case Study

Figure illustrates the test case scenario for our comparative analysis  The scenario is composed of a highway segment with four lanes and the rightmost lane reserved for merging vehicles  The ego vehicle is initialized to follow a slow moving vehicle in lane 1 and has even slower moving traffic to its right in lane 2  Thus, the only option for it, in order to minimize travel time, is to switch left to lane 0 with faster moving traffic and greater headway 
Once it moves to lane 0, and overtakes the slow moving vehicle in lane 1, it has two options: either to keep traveling in lane 0 without making any lane change decisions until getting close to the lead vehicle or proactively exploiting the gap in lane 2 to switch to lane 3 in anticipation of traffic buildup in lanes 1 and 2  A strategic decision maker with foresight will choose to take the later option and make the decision proactively for a greater overall benefit 
The evaluation metrics for the comparative analysis include: travel time, lateral displacement, headway and distance to the closest vehicle  As for the simulation parameters, the simulation step size is set to 0 05s (simulation frequency of 20Hz); the velocities of vehicles in lanes 0, 1 and 2 are set to 8, 5 and 2 m/s respectively while the speed limit V l is set to 15m/s; the length of the highway patch is set to 350m while the width between the lanes is set to 3 5m; and the sensor visibility range is set to R v = 50m 
The parameters for SLAS are set as follows: T s = 0 4s, H = 40, N = 3, A min = −5m/s 2 , A max = 3 5m/s 2 , γ 1 = 1, γ 2 = 0 1 and γ 3 = 0 01  The values of these parameters can be tuned to yield an aggressive or defensive behavior of the algorithm  1) Travel Time: The left plot in Fig  depicts the travel time as a function of longitudinal displacement for the four algorithms 
As seen in the plot, our method (SLAS) maintains a lower overall travel time as compared to the other methods  Quantitatively speaking, SLAS outperforms EA , MOBIL and No-change methods by 12 72%, 23 52% and 54 34% respectively in terms of the time required to complete the simulation scenario  This shows that our method's foresight compensates for its apparent conservativeness arising from the need to preserve passenger comfort 
2) Lateral Displacement: To identify the differences in lane changing behaviors between the four approaches, the relationship between lateral and longitudinal displacements over the course of the simulation is highlighted in the center plot of Fig    In the plot, the lateral displacement of 0 corresponds to the center of lane 0 while the center of each following lane is 3 5m away 
Comparing the performance of the four algorithms, we see SLAS and EA showing relatively similar performances, resulting from proactive decision making  In contrast, since MOBIL only assesses the advantage of switching to the adjacent lanes, it is unable to see the benefit of proactively switching to lane 2  This explains why EA and SLAS start outperforming MOBIL in terms of travel time (left plot) at around the 130 [m] mark for longitudinal displacement 
As for a direct comparison between SLAS and EA , the benefits of having speed advisory system become apparent in this center plot  Due to speed control, SLAS is able to constantly maintain a greater headway (right plot) without having to brake significantly upon getting too close to the lead vehicle  This results in a smooth lateral displacement profile which allows the vehicle to change lanes with minimal jerk (quantitative analysis to follow in Section IV-C) and deliver better overall timing performance (left plot) 
3) Headway: The right plot in Fig  shows the headway maintained by the ego vehicle over the course of the simulation  In accordance with our prior discussion, MOBIL cruises behind the front vehicle, maintaining a relatively low headway until a sufficient space in the adjacent lane is found to perform the lane-change maneuver 
On the other hand, EA and SLAS show a comparable headway trajectory, however, SLAS maintains a greater headway throughout and achieves the maximum headway prior to EA   Quantitatively, SLAS maintains on average 9 43%, 36 57% and 113 17% more headway than the EA , MOBIL and No-change approaches respectively 
This strong performance by SLAS can be attributed to its incorporation of safety guarantees coupled with its consideration for passenger comfort  4) Distance to closest vehicle: Finally, we compare the distance that ego vehicle maintains from the closest vehicle throughout the simulation  On average, SLAS maintains 9 28%, 32 01%, and 22 84% more distance in comparison to EA , MOBIL and No-change approaches respectively 
These numbers are a testament to the strength of our approach resulting from consideration of long planning horizon coupled with speed control 

Monte Carlo Simulations

To demonstrate the long-term performance of the three approaches (SLAS, EA and MOBIL), we run a series of Monte Carlo simulations on scenarios with randomized initial positions (within a range of 8m) and velocities (within    ranges of 8, 5 and 2 m/s assigned to each of the three lanes randomly) of traffic participants 
The result from 50 simulations is presented in Table   In this table, the columns represent the different evaluation metrics, the rows identify the three algorithms, and the values highlighted in green represent the best result with respect to each evaluation metric  The evaluation metrics, going from left to right in the table, are completion time (s), brake (R [−1,0] ), brake jerk (R [−1,0] ), throttle (R [0,1] ), throttle jerk (R [0,1] ), angular acceleration ( • /s 2 ) and angular jerk ( • /s 3 ) 
Apart from completion time, the remaining metrics, based on the commands passed to the vehicular actuators (Fig  ), are used to model passenger comfort  In terms of average performance, SLAS greatly outperforms the other methods when it comes to passenger comfort since it explicitly accounts for comfort in the formulation 
However, it does so at a cost of slightly reduced performance in regards to travel time, when compared to EA , since SLAS tries to strike a balance between minimizing travel time and maximizing passenger comfort  SLAS also secures the lowest standard deviation, for each of the evaluation metrics, when compared to the other methods, which points to the consistency in its long-term performance 

CONCLUSION

We propose a novel behavior planning module for the multi-lane highway maneuvering scenario, that outputs strategic target lane and reference speed commands, and incorporate it with a state-of-the-art motion planning and control framework  We formulate the approach as a receding horizon mixed integer optimization with the goal to minimize travel time while accounting for passenger comfort for a long planning horizon 
In order to reduce the computational overload, we reformulate the problem by replacing integer variables with binary ones and further incorporate various modifications to aid numerical computations  We also carry out a detailed comparative analysis to demonstrate the performance of our approach on the CARLA simulator 
Our future work includes incorporating various delays and uncertainty measures in the perception, localization and prediction modules to evaluate the robustness properties of our approach 

Flooring Constraint

For y ∈ Z and x ∈ R, the constraint y = x can be represented by the following linear constraints: y ≤ x, y + 1 ≥ x + where > 0 accounts for the feasibility tolerance  where M 0 (big-M) and > 0 accounts for numerical errors (chosen to be 0 1 in our implementations) 

Absolute Value Constraint

For ∆s, L s ∈ R, the constraint |∆s| − L s ≥ 0 can be represented as: This can further be generalized, as done in our implementation, to have different forward and rear safety margins as: ∆s ≥ L f s ∨ ∆s ≤ −L r s where L f s and L r s are forward and rear safety margins respectively  This can be represented with the following linear constraints:
s ≥ 0 where M 0 (big-M) and c ∈ {0, 1} is responsible for making a choice between the two constraints 
Context 3: The Official 2006 NBA Draft Early-Entry List  30 International Players, 62 underclassmen and one lone 5th year high school player make up this year's list, for a total of 93 early-entrants  Extensive commentary and early draft status projections are included   For comparison, in 2005, 108 players declared (61 NCAA, 12 high school, 35 internationals), up from 94 in 2004, and 73 in 2003 
There were no major surprises on the early-entry list, besides a few mid-major, Division II and NAIA players that decided to enter, as well as 5th year high school player Clarence Holloway  Amongst the International players, Hrvoje Peric, Renaldas Seibutis, and Kyrylo Fesenko are considered mild surprises who could end up cracking the 2nd round  NCAA Lottery prospects Joakim Noah, Al Horford, Corey Brewer, Josh McRoberts, Brandon Rush and Tyler Hansbrough decided to sit this one out as expected, as did Marco Belinelli, Uros Tripkovic and Goran Dragic on the international front 
Akbar Abdul-Ahad 6-0, PG, Idaho State Junior No Undrafted Averaged under 6 points in 20 minutes per game playing in the Big Sky  Being the first player on the NBA Draft Early-Entry list will likely go down as the highlight of his basketball career 
Arron Afflalo, 6-5, SG, UCLA Sophomore No Undrafted Afflalo initially told the LA media hes returning to school, but after a deep run in the NCAA tournament-- more in spite of his play than because of it--Afflalo will be testing the waters  Afflalo has very average size, athleticism, perimeter shooting and ball-handling skills  Hes clearly receiving bad advice on where his stock lies 
LaMarcus Aldridge, 6-11, PF/C, Texas Sophomore Yes Top 5 pick Aldridge made his announcement official to enter the draft some weeks ago  He will hire an agent soon (Arn Tellem?) and is considered a lock for the top 5 and a strong candidate for #1 overall 
Morris Almond, 6-6, SG, Rice Junior No ??? Almond announced hell be entering the draft, without an agent  He might be the best scorer in the NCAA youve never heard about  His stats are terrific, despite being the sole focal point of opposing defenses, and hes capable of scoring in a variety of ways, particularly with his jumper  Hes hoping for an invite to Orlando 
Renaldo Balkman, 6-8, PF, South Carolina Junior No Undrafted After winning the NIT MVP award, Balkman has decided to see where he stands in the eyes of the NBA by testing the waters  Hes likely to find them downright freezing, as hes a skinny and undersized power forward with little to no skills who came off the bench for a very average team 
Larry Blair,6-1, SG, Liberty Junior No Undrafted The 22 point per game scorer Blair is attempting to get some exposure for himself by testing the waters 
Will Blalock, Iowa State, 5-11, PG, Junior No Second round pick? Declared for the draft together with Curtis_Stinson after Iowa States coach was fired  Size is a big question mark  Will likely hope to attend the pre-draft camp in Orlando and try to show scouts hes a 1st rounder  Likely returns for his senior year 
Jahsha Bluntt, 6-6, SG, Deleware State Junior No Undrafted Puts up fairly average numbers (14 6 ppg, 41% FG) in one of the worst conferences in America  Looking for exposure at the Orlando pre-draft camp but its highly unlikely to receive it 
Josh Boone, 6-10, PF/C, UConn Junior No First round pick? Boone announced hell be entering the draft without an agent  An up and down season has left his stock in the air, and will likely force him to prove himself at the Orlando pre-draft camp  Would greatly benefit from a productive senior season as an offensive focal point now that UConn has lost almost all of its firepower from last year 
Ronnie Brewer, 6-6, PG/SG, Arkansas Junior No Lottery pick? After initially wavering a bit on his decision, Brewer announced hell be entering the draft without an agent in a press conference  Brewer is considered a likely late lottery pick to mid-first rounder pick, as his physical attributes and array of versatile skills on both ends of the floor are highly sought after 
Bobby Brown, 6-1, PG, Cal-State Fullerton Junior No First round pick? DraftExpress exclusively reported that Brown will be testing the waters  Still considered a bit of a sleeper because of the school he plays for, he will not be hiring an agent at this point  Some scouts are very high on his quickness and perimeter shooting ability and feel he will help his stock tremendously in private workouts 
Shannon Brown, 6-4, SG, Michigan State Junior No First round pick? As exclusively reported by DraftExpress, Brown will be testing the waters  He will likely conduct a number of workouts and attend the Orlando pre-draft camp to attempt and gauge where his stock lies  Scouts compare him to Celtic guard Tony Allen, but with a better attitude  Hes a very borderline first rounder in a draft that is stacked with shooting guards 
Derek Burditt, 6-7, SG, Blinn Junior College Sophomore No Undrafted Unknown Junior College prospect  Not ranked as one of the top 25 JUCO players in the country, averaged around 17 points per game  Not burning his draft card as hes not yet an NCAA player, so really doesnt have much to lose, or gain 
Leroy Dawson, 6-2, SG, Emporia State Junior No Undrafted Anonymous Division II player from the MIAA conference  2nd team all conference, averaged 20 points per game  Like MANY on this list, only declaring because he can and has nothing to lose 
Travis DeGroot, 6-4, SG, Delta State Junior No Undrafted Plays in a strong Division II conference, but is at best only the 3rd best prospect on his own team after Jasper Johnson and Jeremy Richardson, and is therefore not a prospect at all 
Guillermo Diaz, 6-2, PG/SG, Miami Junior Yes First round pick? As reported by DraftExpress all year long, Diaz decided to forgo his senior year of college by hiring an agent, Miami based Jason Levien  One of the top athletes and shooters in the draft, which makes for an intriguing combination 
Cem Dinc, 6-10, SF/PF, Indiana Freshman No Undrafted As exclusively reported by DraftExpress, Dinc will be testing the waters  The coach that recruited him and then never played him, Mike Davis, resigned, so it would not shock anyone to see Dinc return to play in Europe and become automatically eligible next year after pulling out of this years draft 
Quincy Douby, 6-3, PG/SG, Rutgers Junior No First round pick As exclusively reported by DraftExpress, Douby sent out his paperwork to enter the draft  NBA scouts are all over the board on him, with some saying they consider him a 2nd round pick and others saying they would not be surprised if he ended up in the lottery  Terrific shooter and shot creator, averaged 28 ppg in the Big East conference  A real sleeper who will likely play in Orlando 
Mike Efevberha, 6-5, SG, Cal State Northridge Junior ??? Undrafted Ramona Shelburne of the LA Daily News reported that Efevberha will be testing the waters  Efevberha was the leading scorer in the country until he had a falling out with his coach and saw his playing time reduced significantly  Hell likely be looking for an invite to the Orlando pre-draft camp, and does not appear to be likely to head back to school 
Carl Elliot, 6-4, PG, George Washington Junior No Undrafted Elliot is using his use it or lose it draft card as a junior to get some exposure for himself through workouts and try to figure out where he stands in the eyes of the NBA  Elliot has excellent size for the PG position, but is still lacking plenty of all-around polish  His senior year will be essential to his development as a player  Reportedly has a family to support, which makes his decision tough considering how old he is already, despite only being a junior 
Jordan Farmar, 6-2, PG, UCLA Sophomore No First round pick? Farmar was the engine that led his team to the Finals of the NCAA tournament, and the only player that showed up once they got there  He is one of the top playmakers in the country, a Steve Nash type point guard, but his average athleticism, defense and outside shooting means hes only a bubble first-rounder  DraftExpress has been on his bandwagon since day one at UCLA, but is the NBA on it too?
Nick Fazekas, 6-11, PF, Nevada Junior No First round pick? Fazekas announced hell be entering the draft without an agent and will likely return to Nevada if it looks like hes not going to be a first round pick  If hes not a first rounder this year, its hard to imagine him ever being one since there isnt much left for him to accomplish individually in the NCAA  An interesting candidate for the pre-draft camp in Orlando 
Thomas Gardner, 6-5, SG, Missouri Junior No Second round pick? The St  Louis Post Dispatch reported that Gardner will enter the draft  Firing of underachieving Missouri coach Quin Snyder appeared to be the straw the broke the camels back  Gardner will have to hope to get invited to Orlando, but moving into the first round appears unlikely without an incredible performance there 
Rudy Gay, 6-8, SF, UConn Sophomore Yes Top 10 pick Gay announced hes leaving UConn at a press conference on campus, with Coach Calhoun by his side  He will hire an agent eventually  Size, length, incredible talent and athleticism means he might have the most upside of any player in this draft  Does he have the fire to capitalize on it though?
Reggie George, 6-10, PF, Robert Morris Chicago (NAIA) Junior No Undrafted Transfer from Iowa State had a nice season in the NAIA and is looking to capitalize on it by gaining some exposure for himself 
Daniel Gibson, 6-2, PG/SG, Texas Sophomore ??? Second round pick As exclusively reported by DraftExpress, Gibson will be entering the draft  There appears to be a conflict between Gibson and Texas regarding what his role will be next year, specifically whether or not hell be playing the point, meaning its unclear whether or not hell be returning  Gibson will likely go to Orlando to help him decide what his next step is  Showing off some PG skills will be essential there 
Aaron Gray, 7-0, Center, Pitt Junior No First round pick? After a disappointing end to his season, being outplayed by Patrick OBryant in the NCAA tournament, Gray has put that behind him and entered his name in the draft without an agent  Hes yet another underclassmen with huge questions marks about his pro potential that will likely have to go to the Orlando pre-draft camp to show he is worthy of a first round pick  Made some great strides this year, but still has a ways to go, especially conditioning-wise 
LeShawn Hammett 6-0, PG, St  Francis Junior No Undrafted Undersized combo guard played only 7 minutes in the mighty Northeast Conference before being suspended indefinitely for conduct detrimental to team  The NBA is clearly the only goal left for him to achieve 
Brandon Heath, 6-3, PG/SG, San Diego State Junior No Second round pick? Streaky shooting combo guard Heath announced the he will test the NBA draft process this summer, and is hoping for an invite to the Orlando pre-draft camp  MWC player of the year; has a lot of wrinkles to his game that need to be ironed out before he can legitimately think about the NBA 
Tedric Hill, 6-10, PF, Gulf Coast Community College Sophomore Yes Undrafted Ineligible to return to school after flunking out of college once again  Has bounced around over the past few years, and received some early hype from wannabe draftniks such as Gregg Doyel (CBS-Sportsline) and Sam Smith (Chicago Tribune) who compare him to Kevin Garnett  Very athletic we're told, but has absolutely no idea how to play the game  Has no chance of being drafted without an amazing showing at the Orlando pre-draft camp 
Clarence Holloway 7-0, Center, IMG Academy (Prep School) 5th year High School No Undrafted Lone high school player in this years age-limit depleted draft  Former Louisville commit never got eligible for college and was always considered too slow and heavy to make much of an impact anyway  Reportedly lost weight and improved his grades this past year at IMG and is currently being recruited by UConn, Kansas State and Oklahoma, amongst others 
Ekene Ibekwe, 6-9, PF, Maryland Junior No Undrafted Sources told DraftExpress exclusively that Ibekwe will be testing the waters  Likely only making this move because he can, as his chances of being drafted are very low  Athletic and long, but still lacking any type of polish 
Donald Jeffers, 6-8, PF, Roxbury Community College Sophomore No Undrafted Anonymous junior college player 
Alexander Johnson, 6-9, PF, Florida State Junior Yes First round pick? Sources told DraftExpress, that Johnson will be hiring an agent, mainly because he is already 23 years old  Hes considered intriguing because of his strength, raw offensive tools and freakish athleticism at the 4 position, and could work his way into the 1st round with strong workouts 
David Johnson, 6-7, PF, Clinton Junior College Sophomore No Undrafted 6-7 JUCO power forward who averaged 2 points and 3 rebounds per game 
Trey Johnson, 6-5, SG, Jackson State Junior No Undrafted Small school prolific scorer and one of the most accurate perimeter shooters in the country will attempt to draw some more attention to himself by testing the waters this summer  Johnson is hoping for a chance to prove himself in the Orlando pre-draft camp in June 
Coby Karl, 6-4, PG/SG, Boise State Junior No Undrafted Son of Denver Nuggets head Coach George Karl put up nice numbers (17 ppg, 5 rebs, 4 assists, 39 5% 3P) in the underrated WAC conference  Had surgery in March to remove a cancerous lump from his thyroid 
Mark Konecny, 6-10, Center, Lambuth (NAIA) Junior No Undrafted Transfer from Syracuse with mediocre production is looking for any type of exposure he can get before he graduates next season 
Kyle Lowry, 6-1, PG, Villanova Sophomore No First round pick NCAA tournament performance showed that he definitely needs another year, but regardless, Lowry is in  For now its without an agent  Considering the lack of quality point guard prospects in this draft, Lowry is likely a first round pick  Says he will attend the Orlando pre-draft camp if invited 
Aleks Maric, 6-11, Center, Nebraska Sophomore No Undrafted As exclusively reported by DraftExpress, Maric will be testing the waters  What may have played a role in this is the fact that the assistant coach that recruited him at Nebraska, Scott Spinelli, just moved on to Wichita State  Maric is considered a very average athlete who is still very raw and is therefore likely to go undrafted should he decide to stay in  Thanks to his Croatian passport, there is money waiting for him overseas if he chooses to take it 
Japhet McNeil, 5-10, PG, East Carolina Junior No Undrafted Severely undersized PG averaged 4 points and 5 6 assists in watered down Conference USA 
Paul Millsap, 6-8, PF, Louisiana Tech Junior Yes First round pick? As expected, Millsap has declared his intentions to enter the NBA draft, and according to sources hired an agent as well  Millsap has likely achieved just about everything he can in college at this point, and will land somewhere in the 20-40 part of the draft depending on workouts and measurements 
Matt Mitchell, 6-0, PG, Southern University-New Orleans Junior No Undrafted Anonymous NAIA player 
Adam Morrison, 6-8, SF, Gonzaga Junior Yes Top 5 pick As DraftExpress exclusively reported that Morrison will be declaring for the draft and hiring Chicago based agent Mark Bartelstein  Morrison, the top scorer in college basketball, is expected to be a top 5 pick and potentially the #1 pick overall  Questions linger about his athleticism and defense, but no one questions his passion, talent or feel for the game 
Patrick O'Bryant, 7-0, Center, Bradley Sophomore Likely First round pick NBA sources in Portsmouth told DraftExpress exclusively that OBryant will be testing the waters without an agent, but is likely to go all the way once he hears that hes a lock for the 1st round  His steady improvement, strong sophomore season, outstanding NCAA tournament and considerable upside means hes probably gone  O'Bryant since confirmed both DraftExpresss reports, particularly the one about hiring an agent in the Tri-State area (Andy Miller) should he decide to go all the way 
Evan Patterson, 6-7, SF, Texas Wesleyan Junior No Undrafted Mediocre numbers (11 ppg, 2 rebs) in a mediocre Southland conference 
Danilo Pinnock, 6-5, SG, George Washington Junior No Undrafted The extremely athletic Pinnock has told GWs student paper hell be testing the waters  Pinnock will attempt to capitalize on his teams success this year by potentially attending the NBA pre-draft camp in Orlando  Pinnock will have to show better ball-handling and perimeter shooting ability than he did during the regular season 
Leon Powe, 6-7, PF, Cal Sophomore No Second round pick Powe announced hell be testing the waters in a statement released by Cal  Where he ends up being projected depends heavily on how his knee checks out  Powe is already considered a serious tweener by NBA scouts, and had a hard time this season gaining back much of the explosiveness he had earlier in his career  Could realistically go undrafted should he decide to stay in 
Richard Roby, 6-5, SG, Colorado Sophomore Likely Second round pick As first indicated by DraftExpress Roby has decided to test the waters  Disappeared against any major competition he went up against, particularly towards the end of the season  Roby will likely have to put on weight in the next few months and show off his perimeter stroke in the Orlando pre-draft camp  Sources tell us that he is on the verge of making a huge mistake by hiring an agent



ground_truth: Reduced computational complexity.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How many underclassmen are on the NBA Draft Early-Entry List?

Read the following text and answer briefly.

Context 3: 
Rajon Rondo, 6-2, PG, Kentucky Sophomore Yes First round pick As expected, Rondo has decided to enter the NBA draft, and has also hired an agent, Bill Duffy  Despite an inconsistent sophomore season, most scouts weve spoken to still had him as at least the #2 point guard on their board because of his intriguing upside  Workouts will be huge for him 
Blake Schilb, 6-7, SG/SF, Loyola Chicago Junior No Undrafted Declared his intentions to enter the draft, without an agent, and is hoping for an invite to Orlando  Schlib is sorely lacking in the quickness and explosiveness departments that scouts demand from swingman prospects, but he makes up for it with his skill set to a certain extent  Regardless, sources tell us he wont be invited to Orlando, meaning he has to go back to school 
Mustafa Shakur, 6-4, PG, Arizona Junior No Second round pick? According to the Arizona Star, Shakur will likely enter his name in the draft, without an agent  Lute Olson confirmed it, saying he is not concerned about it  Shakur is hoping for an Orlando invite to show what he thinks he couldnt at Point Guard U 
Cedric Simmons, 6-9, PF/C, NC State Sophomore No First round pick? Simmons is reportedly "exploring his options," in regards to the 2006 NBA draft, but will do so without an agent  Nice size, frame, length, athleticism and defensive skills make him a very intriguing prospect 
Marcus Slaughter, 6-8, PF, San Diego State Junior Yes Second round pick? After burning his lone draft card a year early last June, despite being considered a marginal prospect, Slaughter has announced that he will be hiring agent Dan Fegan and forfeiting his remaining college eligibility  Slaughters father thinks that There was nothing else for Marcus to do at San Diego State  Many would disagree with that 
Curtis Stinson, 6-3, PG/SG, Iowa State Junior Yes Second round pick After swearing up and down last month that he has no intention on entering the draft, Stinson did just that  His coach Wayne Morgan, who he was very close to, was fired, resulting in him hiring agent Kevin Bradbury  The 23 year old combo guard will have to go to the Orlando pre-draft camp and impress if he wants to come close to being a 1st rounder 
Tyrus Thomas, 6-9, PF, LSU Freshman Yes Top 5 pick As DraftExpress exclusively reported Thomas called a press conference to announce his intentions to enter the 2006 NBA draft, as well as hire agents Brian Elfus and Mike Siegel  SEC Freshman of the year could be the most athletic player in the draft, as well as the player with the most overall upside 
PJ Tucker, 6-5, SF, Texas Junior No Second round pick As reported all year long by DraftExpress, Tucker will be entering the draft without an agent  Considering that hes a 6-5 combo forward with tremendous skills, his stock widely fluctuates depending on who is being asked  Phenomenal basketball player, but is severely lacking in 2-3 inches of height  Will likely need a strong showing at the Orlando pre-draft camp to have a legitimate shot at the 1st round  Some scouts compare him to Bonzi Wells 
Junior No Undrafted Undersized Division II post player has no chance of being drafted despite 20+8 averages 
Ian Vouyoukas, 6-10, Center, St  Louis Junior ??? Undrafted Vouyoukas declared his intentions to enter the draft, supposedly without an agent  Sources in Europe tell us he is likely to return to Greece to take a large contract offer from a first division team once he realizes he has no chance of being drafted  Vouyoukas is a nice mid-major big man who has improved somewhat in his junior season, but does not possess the necessary combination of athleticism and size required of an NBA center 
Darius Washington, 6-2, PG, Memphis Sophomore Likely First round pick? DraftExpress exclusively reported that Washington will be in the draft  It appears that hell be hiring an agent as well, despite not being anywhere near a lock for the first round 
Albert Weber, 6-3, SG, Connors State Sophomore No Undrafted Transfer from Alabama led his conference in scoring and is considered one of the top Junior College players in the country  Not officialy an NCAA player yet, and has not committed to any school yet, so really doesn't stand much to lose (or gain) from this move 
Marcus Williams, 6-3, PG, UConn Junior Yes Late Lottery-Mid-First As expected, Williams is expected to announce that hes hired Calvin Andrews of BDA Sports Management as his agent at a press conference next week  A strong junior season and outstanding NCAA tournament, establishing himself as one of the purest playmakers in the nation, means hes likely one of the first PGs taken 
Andriy Agafonov, 6-8, PF, Khimik 1986 Ukraine Undrafted Ukrainian power forward played 15 minutes and scored 6 points with 4 4 rebounds per game playing for FIBA EuroCup participants, and is declaring in hopes of getting his name out as he has one more draft card to burn after this before becoming automatically eligible 
Nemanja Aleksandrov, 7-0, SF/PF, KK Reflex 1987 Serbia & Montenegro ??? American agent has been telling us all year that hes likely to enter  Still hasnt played a game this year after taking slow recovery process from torn ACL  Once regarded as a prodigy and potential #1 overall pick, but injuries mean he hasnt played in nearly two years and is now considered damaged goods  Might just look for an attractive team to guarantee him in the 2nd round and develop him in the NBDL 
Pape-Philippe Amagou, 6-1, PG, Le Mans 1985 France ??? Amagous American agent has informed us that he will enter the NBA Draft this year, and participate in the Reebok Eurocamp in Treviso  Shares playmaking duties and spotlight with fellow early-entrant Yannick Bokolo 
Andrea Bargnani, 7-0, PF, Benetton Treviso 1985 Italy Top 5 pick Bargnani's Italian agent Stefano Meller told DraftExpress in Portsmouth that the Italian star power forward will definitely be entering the NBA draft  Bargnani is in the process of hiring an American agent and the only question is how long will it take for him to make it over to the US after Benetton finishes up in the Italian playoffs, which could last as far as mid-June  He is expected to be a top 5 pick with a shot at going #1 depending on how the lottery plays out  Considered a phenomenal talent thanks to his excellent size, perimeter skills and athleticism relative to height 
Yannick Bokolo, 6-3, PG/SG, Le Mans 1985 France ??? Terrific athlete who is still making the transition to playing the point full time 
Carlos Cedeno, 6-5, SG, Guaiqueries 1985 Venezuela Undrafted Relatively unknown Venezuelan player  Has some international experience at the junior levels 
Tadija Dragicevic, 6-8, PF, Red Star Belgrade 1986 Serbia & Montenegro Undrafted Undersized power forward barely played in the Adriatic League this past season 
Lior Eliyahu, 6-9, SF/PF, Galil Elyon 1985 Israel Second round pick? Prolific and athletic Israeli combo forward will be entering the NBA draft this year looking for certain guarantees from an NBA team in the 1st or 2nd round  Eliyahu is still in the Israeli army and will stay overseas for another year regardless of what happens  He'll be represented by the American agency Entersport in the United States  A midseason injury set him back from being the top Israeli player in the league despite his youth 
Rudy Fernández, 6-5, SG, DKV Joventut 1985 Spain First round pick? Has some minor buyout issues to deal with to make sure he can stay in the draft  Excellent season in Spain has him projected as a pretty solid first round pick  Improved outside shooting, and still the same excellent athlete, passer, defender and all-around player hes always been  Still very skinny too 
Kyrylo Fesenko, 6-11, PF, Azovmash 1986 Ukraine Second Round Pick More to come 
Rafael Hettsheimeir, 6-9, Center, Akasvayu Girona 1986 Brazil Undrafted Undersized Brazilian center did not overly impress at the Nike Hoop Summit, showing that he will likely lack mobility until he takes off some weight 
Marko Lekic, 6-11, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein, Lekic told us hell be putting his name in the draft this year once again  Still a bit of an unknown, numbers are fairly average in the Serbian YUBA league 
Damir Markota, 6-11, SF/PF, Cibona Zagreb 1985 Croatia Second round pick American agent Marc Cornstein told us Markota will definitely be putting his name in the draft once again  He had a breakout season in the Euroleague and Adriatic league before a groin injury slowed him down and eventually forced him to have minor surgery  Likely wont be able to come to the States until very late in the process  Does not have a buyout 
Mickael Mokongo, 5-11, PG, Chalon 1986 France ??? DraftExpress was exclusively informed hell be in the draft  Considered a talented athlete, but lack of size and the fact that he missed a large chunk of the season due to injury means his draft stock is very much up in the air still 
Brad Newley, 6-6, SG, 1985 Australia Second round pick Newely has told the Australian media that hes entering the draft  Hired Philadelphia based agent Leon Rose  Scouts who saw him play in Argentina last summer like his athleticism  Desperately lacking exposure, but agent appears to be unwilling to provide him with it 
Oleksiy Pecherov, 6-11, PF, Racing Basket 1985 Ukraine Second round pick DraftExpress received indication that Pecherov will be entering his name in the draft after a nice 2nd half regular season in France  Pecherov has his draft card in hand one year before he becomes automatically eligible, meaning he has nothing to lose  Has some nice skills facing the basket, but is still very soft and underdeveloped 
Hrvoje Peric, 6-8, SF, KK Split 1985 Croatia Second round pick? Good athlete who is still coming into his own as a basketball player  Did not play in the Adriatic League this season  Definitely needs at least another year in Europe, but could use the exposure that declaring for the draft provides 
Kosta Perovic, 7-2, Center, Partizan 1985 Serbia & Montenegro Undrafted? DraftExpress has been told that Partizan needs Perovic to be drafted this year to relieve them of his 500,000$ salary next year as well as help them financially with buyout money for their budget  Unfortunately this is happening about 3 years too late as weve seen little to no improvement from Perovic over that span 
Georgios Printezis, 6-9, PF, Olympiakos 1985 Greece Undrafted Greek power forward played 9 minutes and scored 4 points per game playing for a Euroleague team, and is declaring in hopes of getting his name out before he becomes automatically eligible next year 
Milovan Rakovic, 6-10, PF, Atlas 1985 Serbia & Montenegro ??? American agent Marc Cornstein told us Rakovic will be putting his name in the draft  Still an unknown player, puts up nice numbers on occasion in the fairly weak Serbian YUBA league 
Alexandr Rindin, 7-5, Center, Gala Baku 1985 Azerbaijan Undrafted Huge body, complete unknown  5 points, 5 rebounds per game in FIBA Europe Cup 
Sergio Rodríguez, 6-3, PG, Estudiantes 1986 Spain First round pick Rodríguezs agent in the States told DraftExpress exclusively hell be in the draft, likely for good if he gets a commitment in the 1st round  A disappointing start to his season both in Spain and the ULEB cup made this European prodigy point guard fall on most teams draft boards, but Rodríguez picked things up substantially towards the end of the year and is now playing terrific basketball  Weak NCAA PG crop could put him in the lottery with good workouts 
Dusan Sakota, 6-10, SF/PF, Panathinaikos 1986 Greece Undrafted Fairly unathletic perimeter oriented big man was in the draft last year already  Plays for one of the best teams in Europe and rarely sees the floor for meaningful minutes 
Renaldas Seibutis 6-5, SG, Olympiakos 1985 Lithuania Undrafted One of the most productive players in Europe in his age group considering the level he plays at  Important cog on an excellent team, but lacks athleticism and isnt as good of a shooter as you would hope at this point in his career 
Saer Sene, 7-0, Center, Pepinster 1986? Senegal First round pick? Freakishly long and athletic African prospect who played extremely well at the Nike Hoop Summit  Many question his age and lack of productivity in the very average Belgian league A player teams will want to look at closely 
Sidiki Sidibe, 7-1, Center, Levallois 1985 France ??? 7-1, 265 pound volleyball player and former Kansas State commit will be in this years draft according to his American agent  Too raw to get any playing time whatsoever in French 2nd division 
Tiago Splitter, 7-0, PF/C, Tau Vitoria 1985 Brazil Lottery pick Splitters American agent Herb Rudoy told DraftExpress exclusively hes entering the draft Splitter is having a terrific season in both the ACB Spanish League and the Euroleague, but lack of buyout in his contract means he might not be able to stay in  CBA rules allow him to withdraw and become automatically eligible next season  Tau Vitorias president was quoted saying Splitter will be back in Spain next season 
Sun Yue, 6-9, PG/SF, Aoshen 1985 China Second round pick? Super talented tall point guard with decent athleticism and nice defensive skills  Lacks strength and outside shooting ability  Level of competition is mediocre in American semi-pro ABA league, which makes him an intriguing candidate for Orlando pre-draft camp 
Ali Traore, 6-9, PF, Roanne 1985 France ??? Puts up nice numbers in France  Will participate at the Reebok Eurocamp in Treviso 
Ejike Ugboaja, 6-8, PF, Union Bank Lagos 1985 Nigeria Undrafted Plays for Nigerian National Team 
Goran Dragic, 6-4, PG, Geoplin Slovan 1986 Agent initially notified us that Dragic will be entering the draft, but in the end decided to keep him out  His buyout was always a question mark 
Leigh Enobakhare, 6-10, Center, Oostende 1986 Agent Ugo Udezue from BDA Sports Management told us that Enobakhare will be entering the draft  In the end he must have heard that he is not considered a prospect at all, and decided to keep him out of the draft 
Cartier Martin, 6-8, SF/PF, Kansas State Junior Martin pondered entering his name in the draft, especially after the firing of Kansas State coach Jim Wooldridge 
Nick Young, 6-6, SG, USC Sophomore Young told the LA Daily News in February that hes staying at USC for another year 
D J  Strawberry, 6-5, SG/SF, Maryland Junior Strawberry initially intended to test the waters, but eventually ended up not doing so once he found out that his chances of being drafted are almost non-existent 
Al Thornton, 6-7, SF/PF, Florida State Sophomore Implied earlier on in the year that he might put his name in, but sources recently told us it appears that he will return for his senior year  Tallahassee media backs this up 
Marcus Williams (AZ), 6-8, SG/SF, Arizona Freshman After initially appearing to be gone after numerous definitive reports, Williams surprised everyone and thrilled Arizona fans by announcing in a press conference hell be returning for his sophomore year 
Josh McRoberts, 6-11, PF, Duke Freshman After being upset by LSU in the Sweet Sixteen, McRoberts was quoted saying Ill be at Duke next year   Duke issued a press release a month later confirming this 
Yi Jianlian, 7-0, PF, Guangdong 1987? International Jianlian announced in a press conference that hell be staying in China  A CBA official was also quoted on this matter, sounding as if they were the main factor for him staying put 
Acie Law, 6-3, PG, Texas A&M Junior After a fantastic showing in the NCAA tournament, Law helped his NBA draft stock considerably but will return for his senior year where A&M is expected to make a run at possibly winning the Big 12 
Joakim Noah, 6-11, PF/C, Florida Sophomore Huge 2nd half regular of the regular season and NCAA tournament boosted his stock into as high as the top 5  Noah came out and said afterwards hes staying regardless 
Al Horford, 6-9, PF, Florida Sophomore Horford indicated all season long that hes staying at least one more year, but playing extremely well in winning the national championship gave him a realistic chance at being a lottery pick  Regardless, Horford announced he'll return 
Corey Brewer, 6-8, SF, Florida Sophomore Brewer indicated all season long that hes staying at least one more year, but a terrific performance in the NCAA tournament gave him a realistic chance at being a top 20 pick  Regardless, Brewer announced he'll return 
Glen Davis, 6-8, Center, LSU Sophomore Davis announced hell be returning to LSU immediately after an absolutely horrendous showing in the Final Four which exposed all of his glaring weaknesses  Made it official as an LSU press conference alongside Tyrus Thomas 
Jason Smith, 7-0, PF/C, Colorado State Sophomore Smith announced that hes returning for his junior year, stating that "a little further down the road, it [the NBA] might be in my plans  I'm continuing to concentrate on my academics and see how I can help CSU as much as possible "
Jermareo Davidson, 6-10, PF, Alabama Junior > After burning his lone draft card a year early last June, Davidson considered entering the draft again, but eventually made the right decision in announcing hell be returning for his senior year 
Richard Hendrix, 6-8, PF, Alabama Freshman Told Alabama media after NCAA tournament loss that hell be back in Tuscaloosa next year 
Ja'Vance Coleman, 6-3, SG, Fresno State Junior Testing the waters according to the Fresno Bee  Whoops, no hes not 
Sean Singletary, 5-11, PG, Virginia Sophomore Singletary told The Daily Progress in early February that hes returning 



ground_truth: 62.