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 is the rationality coefficient used in the observation model?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Incorporating Human Path Preferences in Robot Navigation with Minimal Interventions
Publish Date: 16 Mar 2023
Author List: Oriana Peltzer, Dylan Asmar, Mac Schwager, Mykel Kochenderfer

Figure

Hyperplane arrangement of a twodimensional space containing two obstacles (colored in gray) The robot is located inside the pink polytope, surrounded by three adjacent obstacle-free polytopes Each hyperplane on the boundary of the robot's polytope corresponds to one of the nonredundant constraints in eq (4) (b)Graph derived from the hyperplane arrangement The nodes on the graph designate polytopes, and edges designate transitions to adjacent polytopes To estimate the human's preference, the robot updates a posterior over the goal and over which of the graph transitions φ 1 , φ 2 and φ 3 is preferred by the human (c)Example preference defined over the graph The location of the goal is indicated in yellow in the lower right polytope For each node, the outgoing pink arrow designates the edge on the graph corresponding to the preferred transition between polytopes 
Simple, 10 × 10, 8 polytopes (b) Map 2: Office, 10 × 10, 56 polytopes (c) Map 3: Classroom, 20 × 20, 73 polytopes (d) Sampled observations and robot's executed trajectories 
Fig 5: Maps used for simulating the robot navigation problem with path preferences In (d), the heading angles observed are indicated with arrows The goal is indicated with a pink circle, and the orange robot corresponds to the starting location The blue robot follows a policy that accounts for path preference, while the green robot does not The opacity of the robots increases with time 
Map 1 problem setup and example realizations for goal-only (green) and path preference (blue) solution methods The robot starts at the lower left corner of the environment, and the goal of the task (pink circle) is in the upper left area The robot does not know which goal, among 10 options (shown in light blue squares), is the correct goal The human provides noisy observations, indicated by arrows, at each iteration The green robot selects actions according to the goal-only baseline, and the blue robot uses our proposed method to infer path preferences The polytopes composing G are drawn in blue Probability of correct goal WLPHVWHS +J (c) Entropy of goal distribution g 
Fig  6: Probability of the correct goal, fig 6b, and entropy of the goal belief distribution P (g), fig 6c, for the same problem setup, fig 6a In this problem instance, the human's preference is to go to the goal by passing on the right side of the obstacle Results are averaged over 50 runs and the area filled represents one standard deviation above and below the mean value The goal-only baseline shows an over-confident prediction (shown by the strong reduction in belief entropy) that the correct goal is less likely, making it more difficult to reach the correct goal compared to a method that accounts for path preference 
Success rates in the simple environment (Map 1) The results are averaged over 6 randomly sampled problem instances (start location, goal location, and goal possibilities), and over 50 runs per problem instance ∆T is the number of time steps separating two consecutive human inputs The robot's mission time is Tmax = 30 time steps We selected γ h = 1 5, corresponding to relatively noisy human inputs and making the problem more difficult to solve for the robot 
Computation times for Goal Only and Path Preference methods on Map 1 (fig 5a),Map 2 (fig 5b), and Map 3 (fig 5c),averaged over 100 runs with randomly sampled problem instances The 95 % confidence interval is provided with the mean We evaluate computation time at the first iteration of each run (where the search depth takes on its highest value Tmax) 

abstract

Robots that can effectively understand human intentions from actions are crucial for successful human-robot collaboration  In this work, we address the challenge of a robot navigating towards an unknown goal while also accounting for a human's preference for a particular path in the presence of obstacles 
This problem is particularly challenging when both the goal and path preference are unknown a priori  To overcome this challenge, we propose a method for encoding and inferring path preference online using a partitioning of the space into polytopes  Our approach enables joint inference over the goal and path preference using a stochastic observation model for the human 
We evaluate our method on an unknown-goal navigation problem with sparse human interventions, and find that it outperforms baseline approaches as the human's inputs become increasingly sparse  We find that the time required to update the robot's belief does not increase with the complexity of the environment, which makes our method suitable for online applications 

INTRODUCTION

Collaboration between humans and robots has become increasingly important and one key aspect of this collaboration is the ability for robots to adapt to human decisions  In many scenarios, such as a robot navigating through a busy room to deliver an item, it is important for the robot to take into account human preferences 
For instance, humans may prefer a specific path that would allow their colleagues to notice the item being delivered, but this preference may change dynamically based on various factors such as changes in the environment or unforeseen circumstances  While some preferences can be incorporated into the path-planning process, accommodating dynamic user preferences in real-time remains challenging 
In this paper, we propose a way to enable robots to adapt to human preferences dynamically by leveraging real-time feedback to inform decision-making  In this work, we tackle the problem of robot navigation in which the robot cannot observe the goal or the preferred path to the goal, but must make navigation decisions that are influenced by humans through recommended actions 
Prior work has explored how to adapt to a human's preference through feedback, but such approaches often require a high level of intervention, which can be time-consuming and impractical in real-world scenarios  To optimize the use of human input and quickly infer the human's preference, Fig  : An autonomous robot navigates in a simulated classroom towards a goal location (pink circle) 
At the start of its mission, it receives direction indications (arrows) from a human that indicate which path it should take to get to the goal  In this scenario, the human wants the robot to go around the desks on the right side of the classroom  A robot that does not reason over path preferences (green) will take the shortest path to the goal regardless of the human's input 
Our method (blue) infers the human's path preference from these indications and adapts to their recommendations  we propose an approach that leverages probabilistic representations of human preference and incorporates real-time feedback  Previous research by Bajcsy et al  considered an online adaptation problem in a manipulation task, where the person can apply forces to the robot to indicate their preferences 
By allowing the robot to continue its task while taking into account a probabilistic representation of human preference, their approach does not require frequent inputs  Building on this idea, we adopt a similar approach to adapt to a human's preference in the context of a robot autonomously navigating through a known environment, such as a cluttered office space 
Specifically, we focus on allowing the human to influence the robot's trajectory with respect to obstacles, by providing guidance on preferred routes or paths, while the robot continues to execute its task  Paths can be represented using homotopy classes   However, homotopies can pose computational challenges when used to encode and infer human preferences 
When the robot maintains a belief over homotopy classes, the inference problem can become exponentially complex with the number of obstacles in the space  Additionally, when the goal is unknown, the number of variables increases with the number of candidate destinations  This complexity can render the decision-making problem intractable 
Our solution is to encode path preference based on a partitioning of the environment into polytopes   This representation allows path preferences to be expressed as sets of preferred transitions between adjacent polytopes  Paths belonging to different homotopy classes correspond to different sequences of transitions 
By leveraging conditional independence assumptions, we can make the Bayesian inference problem tractable  These assumptions exploit the fact that human actions provide information about the path in a piece-wise manner  For example, indicating a preference for navigating around a particular obstacle only provides information about the local area and not the entire path 
Finally, after updating its belief representation over the human's preference, the robot can adapt to indications by replanning online  Our contributions are as follows  • We formulate the human-robot collaboration problem as a Partially Observable Markov Decision Process (POMDP) where both the goal of the task and the human's path preference are unknown random variables 
• We propose an encoding of a human's path preference using a partitioning of the environment into polytopes, along with conditional independence assumptions that make the Bayesian inference problem tractable to infer the task goal and path preference online  • Through simulations in two environments of different sizes and complexity, we show that our method is effective for solving problems where the robot must reach a goal that is unknown a-priori while simultaneously adapting to a human's indications 
Our method shows higher success rates compared to baseline approaches when the human inputs are sparse  Our approach enables a robot to make effective navigation decisions in collaboration with a human, even when the goal and path preference are not known in advance, and with minimal human input  In recent years, there has been a growing interest in shared autonomy and interactive systems, where humans and robots work together to accomplish tasks 
Several approaches have been proposed to address the challenge of enabling effective collaboration between human and robot agents while still achieving high task performance  Losey et al  and Jeon, Losey, and Sadigh propose a framework where a human operator is given control of a task-relevant latent action space while an autonomous system handles the rest 
Dragan and Srinivasa present a formalism for arbitrating between a user's input and a robot's policy when both human and robot share control of the same action space  Cognetti et al  [7] provide a method for real-time modifications of a path,       Fig  : We model the intent inference problem with the above diagram 
At each step in time, the robot receives an observation ot from the human conditioned on its current location st, the intended goal g, and the human's path preference θ  The robot updates its belief over g and θ and transitions to a next location st+1  while Hagenow et al  present a method that allows an outside agent to modify key robot state variables and blends the changes with the original control 
However, a common challenge of these approaches is the high level of intervention required from humans  Best and Fitch propose a method for predicting an agent's intended trajectory from observations  Rather than maintaining a belief over the agent's future path, they infer the agent's intended goal among a set of candidate locations at the boundary of the space 
This approach provides information on where the agent is heading and generates a distribution of candidate future trajectories for the agent  Inferring the goal of the task among a discrete set of candidates is also relevant to the area of shared autonomy  Javdani, Srinivasa, and Bagnell propose a formalism for shared control of a robotic arm, where the robot must assist the human in picking up an object but needs to infer which object the human has chosen from joystick inputs 
Planning with homotopy class constraints is useful in problems where the robot's requirements are given with respect to obstacles, and Yi, Goodrich, and Seppi consider topological constraints provided by human operators  Bhattacharya propose an efficient algorithm for solving pathplanning problems under homotopic constraints 
However, the number of homotopy classes for a given problem can be infinite, and as the robot changes location and updates its representation of the world, carrying out inference over homotopy classes in a dynamic environment requires recomputing the set of homotopies at every iteration, making the belief update challenging 
Prior work has addressed the challenge of shared autonomy by considering how robots can infer a human's intended goal, or how they can infer the preferred path to a goal  However, we argue that inferring the goal and the path as separate problems can lead to over-confidence in incorrect beliefs about the user's preferences 
To illustrate this point, consider the following scenario: a robot and a human are collaborating to move an object from one end of a room to Fig  : Using the hyperplanes composing the H-representation of each obstacle, we construct a hyperplane arrangement of the obstacle-free space (a)  We define the human's preference for the robot's one step action choices as the posterior distribution (given all human input up to that point) over transitions from the current to the neighboring polytopes, i e  edges on the graph 
Each time the robot transitions to a new polytope, the set of neighbor polytopes and the distribution over human preferences are updated  another, but there is an obstacle in the way  The human would like the robot to take a path around the obstacle on the left, even though the goal is on the right  If the robot only infers the goal from the human's inputs, it may incorrectly assume that the goal is on the right, and become over-confident in this belief 
On the other hand, if the robot only infers the preferred path, it may mistakenly assume that the goal is on the left, leading to a failure in completing the task  To overcome these challenges, our work proposes a joint inference approach that considers both the human's intended goal and their preferred path to that goal 
Specifically, we model the human's preference over different homotopy classes and leverage a conditional independence assumption to provide a tractable solution  In our approach, we assume that the human's inputs are noisily rational conditioned on both the goal and the preference  By jointly inferring the goal and path preference, we can avoid over-confidence in incorrect beliefs about the user's preferences, leading to improved system performance 
We consider the problem of robot navigation in a known environment to an unknown destination, where a human can intervene and provide a heading direction to the robot using a joystick or force cues  The human also has a preference on which path the robot should take with respect to obstacles, and our objective is for the robot to understand the human's intentions and execute the task with minimal interventions 
Let g be a discrete random variable denoting the goal of the task, belonging to a set of candidates Ω g , and let θ be a discrete-valued random variable representing the human's path preference, belonging to a set of possible preferences Θ  The physical location of the robot at time index t is denoted by s t ∈ R 2 , and the robot's action at time index t, belonging to some action space A, is denoted by a t  
The transition model T (s t+1 | s t , a t ) is deterministic, meaning the robot has full control over its future location  At any time step, the human may provide an observation to the robot  When the human intervenes, the robot receives a direction (heading angle) that can be mapped to a future location in space 
More specifically, we map the direction to an intended location, which is the resulting robot location after advancing in the indicated direction for one time step  For simplicity, we consider that the robot directly makes an observation o t of the location indicated by the human  We assume that the robot has a stochastic observation model for the human P (o t | s t , g, θ) that is conditioned on both the goal of the task g and the human's preferred path θ 
We further assume that having chosen a goal and path preference, the human takes actions to noisily minimize a cost function C g,θ that measures the cost of moving from the robot's current location to the goal along the preferred path  For example, C g,θ (s t , o t ) can be the length of the shortest path from location s t to the goal g after taking a first step to o t , and constrained by path preference θ 
We use C g,θ to induce a probability distribution over observations, given by: where γ h is a hyperparameter that designates the rationality coefficient  This model assumes the human will pick the lowest cost action with the highest probability and the likelihood of an action decreases exponentially with the increase in cost  
Our inclusion of the path preference θ sets our approach apart from   The model is shown in fig  represented as a Bayesian Network 

Inference

At each time step where the human provides an observation, the posterior P (g, θ) is given through the Bayesian update We note that the number of Bayesian updates required at each iteration to update the belief is equal to the cardinality of Ω g × Θ  In addition, each Bayesian update involves computing C g,θ (  
,   ) in eq  ( ), which involves solving an optimization problem (such as a shortest path problem)  In section IV, we propose a specific encoding of preference θ for resolving eq  ( ), while ensuring the number of computations of the cost C g,θ ( ,  ) per update does not grow exponentially with the number of obstacles 

Decision Making

We consider a navigation problem where the robot receives reward according to the model R(s t , g, θ, a t )  We wish to find the optimal policy π that maximizes the expected discounted sum of future rewards, with discount factor γ  The above problem is a Partially Observable Markov Decision Process (POMDP)  
In this section, we propose an encoding of human's path preference θ for computing the posterior in eq  ( )  Devifrom the concept of homotopy classes, we define the preference according to a partitioning of the environment into polytopes, as shown in fig  , creating a hyperplane arrangement of the space 
Hyperplane arrangements have been used by Vincent and Schwager in the context of Neural Network verification  In our setting, we leverage this representation to define path preferences as preferred transitions between adjacent regions of the space 

Hyperplane Arrangement

We assume a two-dimensional environment composed of m polytopic obstacles, each defined by their half-space representation (H-representation) where A i ∈ R di×2 and b i ∈ R di , and where d i is the number of edges (hyperplanes) composing polytope i  Let n = i d i be the total number of hyperplanes  We leverage each obstacle's H-representation to construct a hyperplane arrangement of the environment as shown in fig 
 e  a partitioning of the space into polytopes  More specifically, each location in space belongs to a polytope j for which we can write an H-representation of the form where α j i ∈ {−1, 1} di is a vector specific to polytope j and obstacle i corresponding to the relative position of any point in the set with respect to each hyperplane in O i  
Fig  : Intent inference model in a hyperplane arrangement of the obstacle free space  We spatially decompose the preference θ into a set of preferred neighboring polytopes per region of the space  Within each polytope j, the human preference pj is a discrete distribution over the preferred neighbor in N (j) 
We assume that for a location st belonging to polytope j, and given goal g and preference pj, the observation ot and any other preference p i,i =j are conditionally independent  Concatenating elements from each obstacle's Hrepresentation, we can write polytope j's H-representation as where Some of the constraints in eq  ( ) (corresponding to rows of A, b and α j ) are redundant, i e  the set P j does not change upon their removal 
We can further reduce the Hrepresentation of a polytope to include only non-redundant constraints  By removing the rows corresponding to redundant constraints, we obtain new matrices A j e , b j e and α j e such that we can write the polytope's reduced H-representation as The non-redundant constraints correspond to edges of the polytope 
In other words, as the robot continually moves in space, the first hyperplane that it will cross upon exiting the polytope will correspond to one of the polytope's nonredundant constraints  Vincent and Schwager outline an iterative method for removing redundant constraints by solving n linear programs 
We use this method in practice for computing α j e for each polytope  We can now characterize each polytope by a vector α j e ∈ {−1, 1} n j e , where n j e ≤ n is the number of essential constraints of the polytope  The polytopes P j partition the environment into a hyperplane arrangement 

Path Preference

In this section, we provide a definition of preference θ according to a graphical representation of the environment based on the hyperplane arrangement  Under this representation, a path preference corresponds to a set of preferred transitions  In other words, for each polytope in the space, the human will have a preference to which neighboring polytope they wish to transition 
Let G := (V, E) be an undirected graph, where vertices are obstacle-free polytopes, and edges connect two adjacent polytopes  Each polytope is described by a unique vector α j as defined in eq  ( )  Two polytopes are adjacent if they share non-redundant constraints (rows in eq  ( )) corresponding to the same hyperplane (i e  they are on opposite sides of the hyperplane) 
Let N (v) be the set of neighbors of a vertex v  For each vertex, we denote p v the discrete-valued random variable describing which edge in N (v) the human intends to transition to  Using this formalism, we define a path preference as the set of preferred transitions over all nodes in the graph, Let m θ = v∈V |N (v)| be the cardinality of Θ, and m g = |Ω g | the number of possible goals 
A priori, the number of Bayesian updates required to update the belief at every iteration should be m θ × m g   Now, let us assume the conditional independence relationships described by the new problem diagram in fig    More specifically, we introduce the assumption that conditioned on a robot location s t , the goal g, and the preference for the corresponding vertex p v in the graph, the observation o t and the preference for any other vertex are conditionally independent 
In other words, the observations the human provides can be defined conditioned only on the robot location, the goal, and the human's preference for its current vertex p v   By introducing this assumption, each update step only requires updating the joint (p v , g), reducing the number of cost computations to |N (v)| × m g  
We can notice that by introducing this assumption, we removed the direct relationship between the number of polytopes in the environment and the complexity of the Bayesian update in eq  ( )  In practice, components of θ are not mutually independent  For example, if the human preference at a vertex v 1 is
, it is unlikely that the human will also prefer p v2 = (v 2 , v 1 ) (turning back)  We can improve our model by assuming a dependent relationship between preferences for adjacent edges, which does not significantly increase the complexity of the inference problem  An interesting property of our encoding is that any two paths that belong to different homotopy classes will cross different sequences of polytopes, i e  they correspond to a different sequence of edges on G 
This can be proved by contradiction  Let us suppose that two continuous trajectories ξ 1 and ξ 2 , with the same start and end points and that do not intersect any obstacle, traverse the same regions in G in the same order  From the construction of the hyperplane arrangement, each polytope that the paths traverse through is obstacle-free 
Therefore, within each polytope, there is no obstacle in the area located in between the portions of ξ 1 and ξ 2 that belong to the region  A smooth transformation of ξ 1 into ξ 2 can be obtained by transforming each portion of ξ 1 belonging to the polytopes it intersects into the corresponding portion of ξ 2 for the same polytopes, where the extremities of the trajectory portions are connected to one another along the polytope's edges (where the same edge is crossed by both paths) 
Along this transformation, the paths do not intersect any obstacle, and therefore ξ 1 and ξ 2 belong to the same homotopy class 

EXPERIMENTS

We evaluate our model on a simulated navigation task where the robot must reach a goal that is unknown a priori while respecting the path preferences indicated by a human  The robot navigates in a grid world containing obstacles  The transition model is deterministic: the robot selects an adjacent location on the grid to reach at the next time step 
The robot is also allowed to take diagonal actions  Each location s t in the map can be mapped to a vertex v t ∈ G  Therefore, the actions leading to locations mapped to different vertices correspond to edges on the graph  We note f (s t , a t ) the edge crossed by taking action a t from location s t  
The robot is given a mission time limit T max for reaching the goal  In this problem, we assume that the human selects actions to noisily minimize a cost function C g,θ , where θ is defined as per eq  ( ), corresponding to the length of the shortest path to the goal constrained by the preference (where the robot is only allowed to make transitions on G along preferred edges) 
More specifically, where δ(s t , g | o t , p vt ) designates the length of the shortest path from s t to g passing by o t and constrained by preference p vt   This is a slight variant of the cost function proposed by Best and Fitch , where we add in a conditioning on the path preference  We compute costs by running the A path planning algorithm on the environment maps (grid worlds with diagonal actions) and impose preference constraints by pruning invalid transitions from the search tree 
Reward model  At each step in time, the robot receives a reward which is a sum of three components: a goal-specific reward a preference-specific reward or penalty We compute solutions to the POMDP defined in section III-B with the online solver POMCP , and with the particularity that within the rollouts, the robot does not expect to collect human inputs 
Each time a solution is computed, the robot takes an action and may receive an observation  If it does, it updates its belief distribution over the unknown problem variables and resolves the POMDP over a receding horizon 

Baselines

• Goal only  The robot solves the POMDP while ignoring the effects of path preference  Similarly to , we assume the human is taking action to minimize a goaldependent cost C g (s t , o t ) = δ(s t , g | o t ), where the conditioning on the preference is removed  We also omit the path preference's contribution to the reward R pref  
• Compliant  The robot complies with the human input, but does not take an initiative  If the user stops providing information, the robot continues in the last direction indicated for 5 time steps (conserving its momentum), then stops  • Blended  We designed an arbitration function to decide between our proposed policy (accounting for path preferences) and the user's recommendation when the robot receives inputs 
Our metric to evaluate confidence in the robot's prediction for the purpose of arbitration is the entropy of the intention distribution H(g, p i ), where p i denotes the preferred neighbor for the current region  Because our representation of the world is discrete, the arbitration is given by a step function 
Denoted by U , the action corresponding to the human's input, and P , the robot's prediction for the optimal action, we write the policy where we chose h = 1 6 as the confidence threshold 

Results

When evaluating the algorithm, we consider that a run is successful if the robot reached the goal within its allocated mission time T max and only made transitions between graph vertices corresponding to the human's preferences  We vary the time delay between human inputs, from constant guidance (∆ T = 1) to only a single observation (∆ T ≥ T max ) 
Success rates  Table I reports the success rates for experiments conducted over six randomly sampled problem instances and 50 runs per instance in Map 1 (fig  )  When the human provides inputs at every iteration, the compliant policy shows the highest success rates  However, as ∆ T increases, the compliant robot is not able to accomplish the task within the allotted time as it does not receive sufficient inputs to do so, and performance decreases compared to the autonomous baselines 
We find that in these runs, accounting for path preference consistently improves performance compared with the goal-only baseline  Results also show that blending the user's input with the robot's policy (Path Preference + Blend) when the human provides information leads to improved performance  Belief entropy 
Figure shows a challenging problem instance where the directions the human provides do not align directly with the shortest path to the goal  By ignoring the effects of preferences in the problem model (goal only), the robot quickly infers from observations that the upper left goal is less likely than others (P (g) drops) 
The strong decrease in entropy shows that the robot becomes overconfident in this prediction  Overconfidence in an incorrect goal will prevent the agent from finding the correct goal once the human's indications directly align with it, as it needs to correct for the wrong predictions, as shown in the path realization (fig 
)  In this realization, the goal-only method (green robot) fails to search the upper left area within the allotted time  By accounting for path preferences in its model, the blue robot's entropy over the goal distribution decreases more steadily, allowing for it to leverage the human's latest observations and reach the goal successfully 
shows an over-confident prediction (shown by the strong reduction in belief entropy) that the correct goal is less likely, making it more difficult to reach the correct goal compared to a method that accounts for path preference  Computation time  In table II we provide the time required to solve the POMDP, and the time required to update the robot's belief as it receives new observations 
We compute solutions on three maps: a simple 10 × 10 grid world with 8 polytopes (fig  ), a 10 × 10 grid world with 56 polytopes (fig  ), and a 20×20 grid world with 73 polytopes (fig  )  The latter environment being larger, we increase the mission time and the depth of the search tree in POMCP from T max = 30 (Map 1 and Map 2) to T max = 60 (Map 3) 
We do not notice an increase in the time required to update the robot's belief with an increase in problem complexity, which is consistent with our observation that the complexity of the Bayesian update should not increase with the number of obstacles or polytopes  On the contrary, the belief update time on Map 2 and Map 3, containing more obstacles, is reduced compared to the first map 
More obstacles result in fewer iterations when solving the constrained shortest path problem with A   Adding constraints due to the obstacles and polytopes reduces the size of the A search tree  C  Limitations Simulation environments  In our simulations, we hardcoded the preference policy over the maps (e g  in Map 1, go around the table counter-clockwise) 
We randomly sampled problem instances (start and goal locations, and goal options) to reduce the bias introduced by these preference choices  To best evaluate and compare the different approaches, it would be best to sample preferences among a distribution of preferences chosen by a human (for example, from benchmarks resulting from a collection of data) 
Creating such a benchmark is an interesting direction for future work  Hyperplane arrangement construction  The main limitation of our approach is that the size and geometry of each polytope depends strongly on the geometry of the obstacles, as seen in fig    Because of this, the robot can make predictions over preferences that are too refined compared with the topology of the environment 
A direct consequence is that when the size of the polytopes is small, the information provided by the human can be incorrectly interpreted as a preference on the robot's immediate action  Our method can be improved by changing the structure of the hyperplane arrangement so that it relies on the topology of the environment, but does not vary strongly with the geometry of the features in the environment 
For this purpose, topometric maps and region construction algorithms are promising directions  We presented an approach for encoding and inferring a human's path preference in an environment with obstacles  By leveraging a partitioning of the space into polytopes and a stochastic observation model, our method allows for joint inference over the goal and path preference even when both are unknown a-priori 
Our experiments on an unknown-goal navigation problem with sparse human interventions demonstrate the effectiveness of our approach and its suitability for online applications  The time required to update the robot's belief does not increase with the complexity of the environment, which further highlights the practicality of our method 



ground_truth: γh.

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 



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 is the vacuum processing system configured in terms of the arrangement of the vacuum processing apparatus?

Read the following text and answer briefly.

Context 3: PROBLEM TO BE SOLVED: To provide a vacuum processor which can suppress the rise of manufacture cost while coping with the enlargement of diameter of a sample, and besides is excellent in maintainability  SOLUTION: This vacuum processor comprises an air loader 1 which is equipped with a plurality of juxtaposed cassette stands 2a and 2b and a carrier 13 for carrying a wafer 20 from or to the cassette stands, a vacuum loader 7 which is equipped with etching treatment chambers 11 and 11b for processing the wafer and a vacuum carriage chamber 16 connected to them through gate valves 15a and 15b, and a lock device 6 consisting of a load lock chamber 6b and an unload lock chamber 6b equipped with gate valves 12a, 12b, 12c, and 12d for connecting the said carrier 13 with the vacuum carriage chamber 16  For the etching treatment chambers, two are provided only on the opposite side of the vacuum carriage chamber symmetrically about the axis line A passing the center, and besides the arrangement positions of the two etching treatment chambers are at an acute angle on the opposite side of the vacuum carriage chamber 
エッチング，ＣＶＤ（化学的気相成長），スパッタリング，アッシング，リンサ（水洗）等の枚葉処理をするのに好適な真空処理装置とそれを用いて半導体デバイスを製造する半導体製造ラインに関するものである。 Those etching, CVD (chemical vapor deposition), sputtering, ashing, to rinser (water washing) suitable vacuum processing apparatus for single wafer processing, such as a semiconductor manufacturing line for manufacturing a semiconductor device using the same  
【従来の技術】試料を処理する真空処理装置は、大別すると、カセットブロックと真空処理ブロックから構成されており、カセットブロックは、半導体製造ラインのベイ通路に面して長手方向に伸びるフロントを有し、試料用のカセットや試料のオリエンテーションを合わせるアライメントユニットと、大気ロボットがある。 BACKGROUND OF THE INVENTION Vacuum processing apparatus for processing a sample, the roughly is constituted by a cassette block and a vacuum processing block, the cassette block, a front extending in the longitudinal direction facing the bay aisle of a semiconductor manufacturing line a, an alignment unit for aligning the cassette and sample orientation for sample, there is an atmospheric robot  真空処理ブロックには、ロード側ロードロック室，アンロード側ロードロック室，真空処理室，後真空処理室，真空ポンプ及び真空ロボット等が設けられている。 The vacuum processing block, the load-side load lock chamber, the unload side load lock chamber, the vacuum processing chamber, a rear vacuum processing chamber, a vacuum pump and a vacuum robot and the like 
【０００３】これらの真空処理装置では、カセットブロックのカセットから取り出された試料が、大気ロボットにより真空処理ブロックのロードロック室まで搬送される。  In these vacuum processing apparatus, a sample taken from the cassette in the cassette block is transported to the load lock chamber in the vacuum processing block by the atmospheric robot  ロードロック室から真空ロボットによりさらに処理室に搬送され、電極構造体上にセットされた試料は、プラズマエッチング等の処理がなされる。 Is conveyed from the load lock chamber to the further processing chamber by the vacuum robot, the sample is set on an electrode structure, processing such as plasma etching is performed  その後、必要に応じて後真空処理室に搬送，処理される。 Thereafter, the conveyance to the rear vacuum processing chamber as necessary and processed  処理済みの試料は、真空ロボット及び大気ロボットによりカセットブロックのカセットに搬送される。 Processed sample is conveyed to the cassette of the cassette block by the vacuum robot and the atmospheric robot 
【０００４】試料をプラズマエッチング処理する真空処理装置の例としては、例えば特公昭61−8153号公報，特開昭63−133532号公報，特公平6−30369号公報，特開平  Examples of the sample vacuum processing apparatus for plasma etching treatment, for example Japanese Patent Publication 61-8153, JP-Sho 63-133532 and JP Kokoku 6-30369, JP-A No 
6−314729号公報，特開平6−314730号公報，米国特許第 6-314729, JP-A No  6-314730, JP-U S  Patent No 
5,314,509号明細書および5,784,799号明細書に記載されたようなものがある。 There are such as described in Pat and 5,784,799 Pat 5,314,509 
509号明細書に記載された装置は、真空処理ブロックの中央付近に真空ロボット、その周囲に３個の処理室が同心状に配置され、真空ロボットとカセットブロックの間に、ロード側ロードロック室，アンロード側ロードロック室が設けられている。 Device described in 509 Pat are vacuum robot in the vicinity of the center of the vacuum processing block, three process chambers around it are arranged concentrically, between the vacuum robot and the cassette block, the load-side load-lock chamber , unload side load lock chamber is provided  これらの装置では、大気ロボットや真空ロボットの搬送アームの回転角度が大きく従って装置全体の必要床面積が大きいという問題がある。 In these devices, there is a problem that needs floor space for the entire rotation angle is large therefore device of the transfer arm of the atmospheric robot and the vacuum robot is large 
【０００６】一方、真空処理装置の真空処理ブロック内の処理室や真空ポンプその他各種の配管機器については、定期，不定期に点検修理等のメンテナンスを行うことが必要である。  On the other hand, the processing chamber and the vacuum pump and other various piping components of the vacuum processing block of the vacuum processing apparatus, periodically, it is necessary to perform maintenance such as inspection and repair irregularly  そのため、一般に、真空処理ブロックの周囲には、扉が設けられており、この扉を開けることにより、ロードロック室，アンロードロック室，処理室，真空ロボット及び各種の配管機器の点検修理ができるようになっている。 Therefore, in general, around the vacuum processing block, the door is provided by opening the door, load lock chambers, unload lock chambers, processing chambers, the servicing of the vacuum robot and various piping devices It has become way 
キャリアポッドが必要となるために、約３５０mm程度と大きくなり、複数のキャリアポッドを収納するカセットブロックの幅も大きくなる。 For carrier pod required, large as about 350 mm, the greater width of the cassette block for housing a plurality of carrier pods  この幅に合わせて真空処理ブロックの幅を決定すると、真空処理装置全体が大きなスペースを必要とすることになる。 When determining the width of the vacuum processing block in accordance with the this width, the entire vacuum processing apparatus requires a large space  一例として、４個のキャリアポッドを収納するカセットブロックについて考えると、試料の直径ｄが８インチから１２インチになった場合、カセットの幅は少なくとも約４０cm以上大きくならざるを得ない。 As an example, considering the cassette block for accommodating four carriers pods, if the diameter d of the sample was a 12-inch 8 inch wide cassette inevitably increases at least about 40cm or more 
【０００８】一方、試料に各種の処理を行いながら大量の処理を行うために、一般の半導体製造ラインでは、同じ処理を行う複数の真空処理装置を同じベイに集め、各ベイ間の搬送を自動またはマニュアルで行っている。 On the other hand, in order to perform a lot of processing while performing various processes in the sample, in a general semiconductor manufacturing lines, gathering a plurality of vacuum processing apparatus for performing the same processing in the same bay, automatic conveyance between the bays or it is carried out manually  このような半導体製造ラインは、高いクリーン度を必要とするため、半導体製造ライン全体が大きなクリーンルーム内に設置される。 Such a semiconductor manufacturing line, requires a high degree of cleanliness, the whole semiconductor manufacturing line is placed in a large clean room  試料の大口径化に伴う真空処理装置の大型化は、クリーンルーム占有面積の大型化を伴うが、これはもともと建設コストの高いクリーンルームの建設コストを一層増加させることになる。 Size of the vacuum processing apparatus due to the large diameter of the sample is accompanied by a large clean room area occupied, which will be further increased construction costs of the high construction cost clean room originally  もし、同じ面積のクリーンルームに占有面積の大きな真空処理装置を設置するとすれば、真空処理装置の全体の台数を減らすか、あるいは各真空処理装置間の間隔を狭くせざるを得ない。 If, if the clean room of the same area to install a large vacuum processing apparatus of the occupied area, reduce the overall number of the vacuum processing apparatus, or interval narrower forced between the vacuum processing apparatus  同じ面積のクリーンルームにおける真空処理装置の設置台数減少は、必然的に半導体の製造ラインの生産性の低下ひいては半導体の製造コストの上昇を伴う。 Installed base reduction in the vacuum processing apparatus in a clean room having the same area is accompanied inevitably rise of the semiconductor decrease and thus the semiconductor manufacturing cost of productivity of the production line  他方、各真空処理装置間の間隔を狭くすることは、点検修理のためのメンテナンススペースが不足し、真空処理装置のメンテナンス性を著しく阻害する。 On the other hand, to reduce the distance between each of the vacuum processing apparatus, the maintenance space for inspection and repair is insufficient to significantly inhibit the maintenance of the vacuum processing apparatus 
【０００９】本発明の目的は、試料の大口径化に対応しつつ、製造コストの上昇を抑えることのできる真空処理装置を提供することにある。 An object of the present invention, while corresponding to the large diameter of the sample, is to provide a vacuum processing apparatus capable of suppressing an increase in manufacturing cost 
【００１０】本発明の他の目的は、試料の大口径化に対応しつつ、メンテナンス性に優れた真空処理装置を提供することにある。 Another object of the present invention, while corresponding to the large diameter of the sample is to provide a vacuum processing apparatus having excellent maintainability 
【００１１】本発明の他の目的は、試料の大口径化に対応しつつ、真空処理装置の必要設置台数を確保して製造コストの上昇を抑え、かつ、メンテナンス性も損なわない半導体製造ラインを提供することにある。 Another object of the present invention, while corresponding to the large diameter of the sample, to ensure the necessary number of installed vacuum processing apparatus suppressing an increase in manufacturing cost, and a semiconductor manufacturing line is not impaired maintainability It is to provide 
【課題を解決するための手段】本発明は、並設した複数のカセット台およびカセット台から、あるいはカセット台へウエハを搬送するための搬送装置を備えた大気ローダと、ウエハを処理するための真空処理室およびこれにゲート弁を介して連接された真空搬送室を備えた真空ローダと、前記搬送装置と前記真空搬送室とを連接するためのゲート弁を備えたロードロック室およびアンロードロック室からなるロック装置とを含んで構成される真空処理装置において、ウエハを処理するための真空処理室は、有磁場ＵＨＦ帯電磁波放射放電方式リアクタ（以下、ＵＨＦ−ＥＣＲリアクタという。）によって形成される真空処理室であり、該真空処理室には、分解可能な側壁インナーユニットおよびアンテナが設けられ、該真空処理室は、真空搬 The present invention SUMMARY OF THE INVENTION from a plurality of cassette tables and cassette stand juxtaposed, or the atmosphere loader having a conveying device for conveying the wafer to the cassette table, for processing a wafer the load lock chamber and the unload lock having a gate valve for connecting the vacuum loader vacuum processing chamber and having a vacuum transfer chamber which is connected via a gate valve to the said vacuum transfer chamber and the conveying device in the vacuum processing apparatus configured to include a lock device comprising a chamber, a vacuum processing chamber for processing a wafer it is formed by a magnetic field UHF band electromagnetic wave radiation discharge type reactor (hereinafter  referred UHF-ECR reactor) that a vacuum processing chamber, the vacuum processing chamber, degradable sidewall inner unit and an antenna are provided, the vacuum processing chamber, a vacuum transportable 室およびロック装置の中央を通る軸線に対して対称にして、かつ真空搬送室を中心にしてロック装置の反対側のみに２つ設けられ、かつ真空搬送室に対して前記２つの真空処理室の配置位置は鋭角をなしている真空処理装置を提供する。 And symmetrically with respect to the axis passing through the center of the chamber and the locking device, and the opposite side only two provided for to lock device around the vacuum transfer chamber, and the two vacuum processing chamber to the vacuum transfer chamber position is to provide a vacuum processing apparatus which forms an acute angle 
ＣＲリアクタによって形成される真空処理室であり、該真空処理室は、真空搬送室およびロック装置の中央を通る軸線に対して対称にして、かつ真空搬送室を中心にしてロック装置の反対側のみに２つ設けられ、かつ真空搬送室に対して前記２つの真空処理室の配置位置は鋭角をなしており、ＵＨＦ−ＥＣＲのアンテナは、前記軸線に対して平行で、かつ前記真空搬送室とは反対側に開放される真空処理装置を提供する。 A vacuum processing chamber formed by a CR reactor, vacuum processing chamber, and symmetrically with respect to the axis passing through the center of the vacuum transfer chamber and the locking device, and the opposite side of the locking device around the vacuum transfer chamber only two provided, and positions of the two vacuum processing chamber to the vacuum transfer chamber is an acute angle, the UHF-ECR antennas, and parallel, and the vacuum transfer chamber with respect to said axis to provides a vacuum processing apparatus is opened to the opposite side 
かつ真空搬送室を中心にしてロック装置の反対側のみに２つ設けられ、かつ真空搬送室に対して前記２つの真空処理室の配置位置は鋭角をなしており、大気ローダ，真空ローダおよびロック装置はＴ字配置とされた真空処理方法を提供する。 And around the vacuum transfer chamber opposite only two provided a locking device, and positions of the two vacuum processing chamber to the vacuum transfer chamber is an acute angle, atmospheric loader, vacuum loader and locking apparatus to provide a vacuum processing method which is a T-arrangement 
【００１５】本発明は、並設した複数のカセット台およびカセット台から、あるいはカセット台へウエハを搬送するための搬送装置を備えた大気ローダと、ウエハを処理するための真空処理室およびこれにゲート弁を介して連接された真空搬送室を備えた真空ローダと、前記搬送装置と前記真空搬送室とを連接するためのゲート弁を備えたロードロック室およびアンロードロック室からなるロック装置とを含んで構成される真空処理装置が平行に複数台並設された真空処理システムにおいて、ウエハを処理するための真空処理室は、ＵＨＦ−ＥＣＲリアクタによって形成される真空処理室であり、該真空処理室は、真空搬送室およびロック装置の中央を通る軸線に対して対称にして、かつ真空搬送室を中心にしてロック装置の反対側のみに２ The present invention, a plurality of cassette tables and cassette stand juxtaposed, or the atmosphere loader having a conveying device for conveying the wafer to the cassette base, the vacuum processing chamber for processing the wafer and to a vacuum loader having a vacuum transfer chamber which is connected via a gate valve, the locking consisting of the conveying device and the load lock chamber and the unload lock chamber with gate valves for connecting the said vacuum transfer chamber apparatus and in the vacuum processing system vacuum processing apparatus is constituted with a plurality Tainami set in parallel include vacuum processing chamber for processing a wafer is vacuum processing chamber formed by the UHF-ECR reactor, vacuum processing chamber, and symmetrically with respect to the axis passing through the center of the vacuum transfer chamber and the locking device, and 2 only on the opposite side of the locking device around the vacuum transfer chamber 設けられ、かつ真空搬送室に対して前記２つの真空処理室の配置位置は鋭角をなしており、並設された複数の真空処理装置のすべての真空処理室に一直線上に配列される真空処理システムを提供する。 Provided, and positions of the two vacuum processing chamber to the vacuum transfer chamber is an acute angle, a vacuum process that is arranged in alignment with all of the vacuum processing chamber of a plurality of vacuum processing apparatus are arranged in parallel to provide a system 
【発明の実施の形態】以下、本発明にかかる一実施例を図面に基づいて説明する。 BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, will be explained based on an embodiment according to the present invention with reference to the accompanying drawings 
１０Ｂ，１０Ｃで示す。 10B, shown by 10C 
【００１８】図１に示す真空処理システムを説明する前に、図２から図４に基づいて真空処理装置を説明する。  Before describing the vacuum processing system shown in FIG  1, illustrating a vacuum processing apparatus on the basis of FIGS  2-4 
５ｂを介して連接された真空搬送室１６を備えた真空ローダ７と、前記搬送装置と前記真空搬送室とを連接するためのゲート弁を備えたロードロック室６ａおよびアンロードロック室６ｂからなるロック装置６とを含んで構成される。 5b a vacuum loader 7 having a vacuum transfer chamber 16 which is connected via consist load lock chamber 6a and the unload lock chamber 6b has a gate valve for connecting and said vacuum transfer chamber and the conveying device configured to include a locking device 6 
平行形に配置され、その位置および姿勢を変えることなく、装置への導入／払出しが可能な位置、すなわち、カセット１ａないし１ｃを略水平の平面上で常に一定の位置に固定される。 Arranged parallel type, without changing its position and orientation, the introduction / dispensing of the locations of the device, i e , always fixed to a constant position to free the cassette 1a 1c on a substantially horizontal plane  カセット台２ａおよび２ｂは、平行に隣合わせて配置してある。 Cassette tables 2a and 2b, are arranged side by side in parallel  カセット台２ｃは、最右端に配置してある。 Cassette table 2c is, are arranged on the top right edge  カセット１ａおよび１ｂは、処理を行うための末処理ウエハを収納したり、処理済みのウエハを回収するためのもので、複数枚（通常２５枚）の被処理基板であるウエハ２０が収納可能となっている。 Cassettes 1a and 1b, or storing the processed wafers end for processing, for recovering the processed wafer, the wafer 20 is a substrate to be processed in a plurality (25 pieces Normal) can be stored going on  カセット１ｃは、この場合、プラズマを用いたドライクリーニング（以下、「プラズマクリーニング」という。）を行うためのダミーウエハを収納したり、プラズマクリーニング後のダミーウエハを回収するためのもので、複数枚（通常２５枚）のダミーウエハ３０が収納可能となっている。 Cassette 1c in this case, dry cleaning using plasma (hereinafter, "plasma cleaning" hereinafter ) Or housing a dummy wafer for performing, for recovering the dummy wafers after plasma cleaning, a plurality (usually dummy wafer 30 of 25 sheets) has become can be stored 
とカセット１ａ，１ｂとの間でウエハ２０を、そしてロードロック室６ａおよびアンロードロック室６ｂとカセット１ｃとの間でダミーウエハ３０を授受可能に動作する。 A cassette 1a, the wafer 20 with the 1b, and operates to allow exchanging dummy wafer 30 between the load lock chamber 6a and the unload lock chamber 6b and the cassette 1c 
ａ，１５ｂを介して真空処理室であるエッチング処理室１１ａ，１１ｂが設けてある。 a, etching chamber is a vacuum processing chamber through a 15b 11a, 11b are provided  以下、エッチング処理室を例に取って説明する。 Hereinafter will be described taking the etching chamber as an example  真空搬送室１６内には、ロードロック室６ａ，アンロード室６ｂおよびエッチング処理室１１ａ，１１ｂとの間でウエハ２０またはダミーウエハ３０を授受可能に動作する搬送装置１４が設けてある。 The vacuum transfer chamber 16, load lock chambers 6a, unload chamber 6b and etching chambers 11a, conveying device 14 is provided that operates to allow transferring the wafer 20 or dummy wafer 30 between 11b  真空搬送室１６は、独立に真空排気可能な真空排気装置１７を装備している。 Vacuum transfer chamber 16 is equipped with a vacuum evacuable vacuum evacuation device 17 independently 
【００２２】ＵＨＦ−ＥＣＲリアクタのエッチング処理室１１ａ，１１ｂは、この場合、同一の構成で対称配置とされてエッチング処理が行われるようになっている。 The UHF-ECR reactor etching chambers 11a, 11b is in this case are symmetrically arranged in the same configuration so that the etching process is performed 
エッチング処理室１１ａを例に説明する。 The etching chamber 11a will be described as an example  エッチング処理室１１ａは、ウエハ２０を配置するための試料台を有し、試料台８ａの上部に放電部７ａを形成するように放電室が設けてある。 Etching chamber 11a has a sample stage for placing the wafer 20, the discharge chamber so as to form a discharge portion 7a at the top of the sample table 8a is provided  エッチング処理室１１ａは、放電部７ａへの処理ガス供給のためのガス導入装置１０ａを有するとともに、エッチング処理室１１ａ内を所定圧力に減圧排気する真空排気装置９ａを有し、放電部７ａの処理ガスをプラズマ化するための、この場合、ＵＨＦ波と磁場の発生手段を有している。 The etching chambers 11a, which has a gas introduction device 10a for processing the gas supply to the discharge portion 7a, having a vacuum exhaust device 9a for evacuating the etching chamber 11a to a predetermined pressure, the process of the discharge portion 7a for plasma gas, in this case, it has a generating means of the UHF wave and magnetic field 
９は、センサ１８からの計測値を所定値と比較して、エッチング処理室内のクリーニング時期を判断する。 9, the measured value from the sensor 18 is compared with a predetermined value to determine the cleaning time of the etching chamber  また、制御装置１９は、真空搬送装置１３および１４を制御して、ダミーウエハ３０をカセット１ｃおよびエッチング処理室１１ａないし１１ｂの間で搬送制御する。 The control device 19 controls the vacuum transfer apparatus 13 and 14, to the dummy wafer 30 to the cassette 1c and the etching process chamber 11a carrying controlled between 11b 
いずれかの方法によりウエハ処理またはプラズマクリーニングを実行する。 It executes the wafer processing or plasma cleaning by any method 
によって、カセット１ａ内のウエハ２０を下から順にエッチング処理室１１ａ，１１ｂに搬入し、それぞれのウエハ２０をエッチング処理する。 Accordingly, the wafer 20 to an etching treatment chamber in order from the bottom 11a of the cassette 1a, and carried into 11b, and each of the wafer 20 is etched  処理されたそれぞれのウエハ２０は、真空搬送装置１４および搬送装置１３によって、カセット１ａ内の元の位置に収納する。 Each wafer 20 processed by vacuum transfer apparatus 14 and the carrier 13 is housed in its original position in the cassette 1a  この場合、運転開始から終了に至る間、カセットの位置および姿勢を変えることなく未処理のウエハを取り出し、そして処理済みのウエハを未処理のウエハが収納されていた元の位置に戻して収納する。 In this case, while leading to completion of the start of the operation, it takes out an unprocessed wafer without changing the position and posture of the cassettes, and the processed wafer unprocessed wafer is accommodated back into position which is housed   このようにすることで、生産ラインの自動化への対応が容易で、且つ、ゴミの発生によるウエハの汚染を低減でき、高い生産効率と高い製品歩留まりを実現できる。 In this way, it is easy to respond to automation of production lines, and can reduce the contamination of the wafer due to the generation of dust can achieve high production efficiency and high product yield 
の処理が全て終り次のカセットの内のウエハ処理に移る前でも良い。 Of the process is good, even before moving on to the wafer processing of all end next cassette 
【００２７】プラズマクリーニングの実施にあたっては、次の順序で行われる。 The practice of plasma cleaning is performed in the following order  この場合、カセット１ｃに収納されたダミーウエハ３０（この



ground_truth: Multiple vacuum processing apparatuses are arranged in parallel.