Patent Description:
Vehicles operating in an autonomous mode (e.g., driverless) can relieve occupants, especially the driver, from some driving-related responsibilities. When operating in an autonomous mode, the vehicle can navigate to various locations using onboard sensors, allowing the vehicle to travel with minimal human interaction or in some cases without any passengers.

Motion planning and control are critical operations in autonomous driving. However, in certain situations, the prediction of the position of the obstacle in the future may not be so confident. It may be challenging to plan the trajectory of the ADV to avoid a collision with the obstacle. <CIT> relates to a method for creating a probabilistic free space map with static (2a, 2b, <NUM>) and dynamic objects (V1-V7), having the following steps: retrieving (S1) static objects (2a, 2b, <NUM>) as well as a perception area polygon (WP) from an existing environment model; collecting (S2) predicted trajectories (T1, T2) of dynamic objects (V1-V7);merging (S3) the static objects (2a, 2b, <NUM>) of the perception area polygon (WP) and the predicted trajectories (T1, T2) in a first free space map; fixing (S4) a maximum prediction time; fixing (S5) prediction time steps; fixing (S6) a current prediction time and setting this current prediction time to the value <NUM> in order to fix the start of a fixed prediction time period; fixing (S7) confidence regions (K) around the static (2a, 2b, <NUM>) and dynamic objects (V1-V7);fixing (S8) at least one uncertain region (U) around at least one static (2a, 2b, <NUM>) or dynamic object (V1-V7);producing (S9) a first probabilistic free space map for the current prediction time; producing (S10) at least one further free space map for at least one prediction time step; evaluating (S11) the produced free space maps. <CIT> describes systems and methods for a dual buffer zone system to ensure a stable nudge for autonomous driving vehicles. In one embodiment, a system perceives a driving environment surrounding an autonomous driving vehicle (ADV), including perceiving one or more obstacles within a view of the ADV. For each of the one or more obstacles, if a previous planning decision for the obstacle is not a nudge, the system associates a first buffer zone with the obstacle. Otherwise, the system associates a second buffer zone with the obstacle. Based on the associated buffer zone for the obstacle, the system determines a planning decision to nudge the obstacle to ensure a buffer distance between the ADV and the obstacle. The system generates a trajectory for the ADV based on the planning decisions for the one or more obstacles.

In an aspect, there is provided a computer-implemented method for operating an autonomous driving vehicle (ADV), the method including:.

In another aspect, there is provided a non-transitory machine-readable medium having instructions stored therein, which when executed by a processor, cause the processor to perform operations of the method described above.

In another aspect, there is provided a data processing system, including a processor and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations of the method described above.

With the invention, the ADV may drive safely according to the planned trajectory to avoid a collision with the obstacle. The uncertainty of predicting a future position of the obstacle may be handled and the safety of driving may be improved.

Various embodiments and aspects of the invention will be described with reference to details discussed below, and the accompanying drawings will illustrate the various embodiments. Numerous specific details are described to provide a thorough understanding of various embodiments of the invention. However, in certain instances, well-known or conventional details are not described in order to provide a concise discussion of embodiments of the invention.

According to some embodiments, when the prediction is not so confidence about the speed of an obstacle in future, a distribution of possible speeds of the obstacle may be predicted. Based the distribution of possible speeds of the obstacle, a distribution of possible positions of the obstacle may be predicted. The distribution of possible positions may be utilized to determine the region that the obstacle may exist. The obstacle's future shape may be modified based on the region instead of a fixed position. The ADV may plan a trajectory based on the modified future shape of the obstacle to avoid a collision with the obstacle to drive safely.

According to some embodiments, an obstacle is detected based on sensor data obtained from a plurality of sensors of the ADV. A distribution of a plurality of positions of the obstacle at a point of time may be predicted. A range of positions of the plurality of positions of the obstacle may be determined based on a confidence level of the range. A modified shape with a modified length of the obstacle may be determined based on the range of positions of the obstacle. A trajectory of the ADV based on the modified shape with the modified length of the obstacle may be planned. The ADV may be controlled to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

In one embodiment, the modified shape with the modified length of the obstacle includes an elongated shape longer than an actual length of the obstacle. In one embodiment, the determining a predicted range of positions of the plurality of positions of the obstacle includes determining the predicted range of positions of the plurality of positions of the obstacle according to a history of velocities of the obstacle.

In one embodiment, the confidence level of the range corresponds to a probability of the obstacle will be within the range. In one embodiment, the predicting a distribution of a plurality of positions of the obstacle in a point of time further includes predicting multiple distributions of a plurality of positions of the obstacle in multiple points of time, each distribution of the plurality of positions of the obstacle corresponding to one point of time.

In one embodiment, the obstacle is a leading obstacle, and the planning a trajectory of the ADV based on the modified shape with the modified length of the obstacle including planning to stop the ADV before a lower bound of the modified shape with the modified length of the obstacle. In one embodiment, the obstacle is a leading obstacle, and the planning a trajectory of the ADV based on the modified shape with the modified length of the obstacle including planning to pass the obstacle on a side based on an upper bound of the modified shape with the modified length of the obstacle.

<FIG> is a block diagram illustrating an autonomous driving network configuration according to one embodiment of the invention. Referring to <FIG>, network configuration <NUM> includes autonomous driving vehicle (ADV) <NUM> that may be communicatively coupled to one or more servers <NUM>-<NUM> over a network <NUM>. Although there is one ADV shown, multiple ADVs can be coupled to each other and/or coupled to servers <NUM>-<NUM> over network <NUM>. Network <NUM> may be any type of networks such as a local area network (LAN), a wide area network (WAN) such as the Internet, a cellular network, a satellite network, or a combination thereof, wired or wireless. Server(s) <NUM>-<NUM> may be any kind of servers or a cluster of servers, such as Web or cloud servers, application servers, backend servers, or a combination thereof. Servers <NUM>-<NUM> may be data analytics servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc..

An ADV refers to a vehicle that can be configured to in an autonomous mode in which the vehicle navigates through an environment with little or no input from a driver. Such an ADV can include a sensor system having one or more sensors that are configured to detect information about the environment in which the vehicle operates. The vehicle and its associated controller(s) use the detected information to navigate through the environment. ADV <NUM> can operate in a manual mode, a full autonomous mode, or a partial autonomous mode.

In one embodiment, ADV <NUM> includes, but is not limited to, autonomous driving system (ADS) <NUM>, vehicle control system <NUM>, wireless communication system <NUM>, user interface system <NUM>, and sensor system <NUM>. ADV <NUM> may further include certain common components included in ordinary vehicles, such as, an engine, wheels, steering wheel, transmission, etc., which may be controlled by vehicle control system <NUM> and/or ADS <NUM> using a variety of communication signals and/or commands, such as, for example, acceleration signals or commands, deceleration signals or commands, steering signals or commands, braking signals or commands, etc..

Components <NUM>-<NUM> may be communicatively coupled to each other via an interconnect, a bus, a network, or a combination thereof. For example, components <NUM>-<NUM> may be communicatively coupled to each other via a controller area network (CAN) bus. A CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host computer. It is a message-based protocol, designed originally for multiplex electrical wiring within automobiles, but is also used in many other contexts.

Referring now to <FIG>, in one embodiment, sensor system <NUM> includes, but it is not limited to, one or more cameras <NUM>, global positioning system (GPS) unit <NUM>, inertial measurement unit (IMU) <NUM>, radar unit <NUM>, and a light detection and range (LIDAR) unit <NUM>. GPS system <NUM> may include a transceiver operable to provide information regarding the position of the ADV. IMU unit <NUM> may sense position and orientation changes of the ADV based on inertial acceleration. Radar unit <NUM> may represent a system that utilizes radio signals to sense objects within the local environment of the ADV. In some embodiments, in addition to sensing objects, radar unit <NUM> may additionally sense the speed and/or heading of the objects. LIDAR unit <NUM> may sense objects in the environment in which the ADV is located using lasers. LIDAR unit <NUM> could include one or more laser sources, a laser scanner, and one or more detectors, among other system components. Cameras <NUM> may include one or more devices to capture images of the environment surrounding the ADV. Cameras <NUM> may be still cameras and/or video cameras. A camera may be mechanically movable, for example, by mounting the camera on a rotating and/or tilting a platform.

Sensor system <NUM> may further include other sensors, such as, a sonar sensor, an infrared sensor, a steering sensor, a throttle sensor, a braking sensor, and an audio sensor (e.g., microphone). An audio sensor may be configured to capture sound from the environment surrounding the ADV. A steering sensor may be configured to sense the steering angle of a steering wheel, wheels of the vehicle, or a combination thereof. A throttle sensor and a braking sensor sense the throttle position and braking position of the vehicle, respectively. In some situations, a throttle sensor and a braking sensor may be integrated as an integrated throttle/braking sensor.

In one embodiment, vehicle control system <NUM> includes, but is not limited to, steering unit <NUM>, throttle unit <NUM> (also referred to as an acceleration unit), and braking unit <NUM>. Steering unit <NUM> is to adjust the direction or heading of the vehicle. Throttle unit <NUM> is to control the speed of the motor or engine that in turn controls the speed and acceleration of the vehicle. Braking unit <NUM> is to decelerate the vehicle by providing friction to slow the wheels or tires of the vehicle. Note that the components as shown in <FIG> may be implemented in hardware, software, or a combination thereof.

Referring back to <FIG>, wireless communication system <NUM> is to allow communication between ADV <NUM> and external systems, such as devices, sensors, other vehicles, etc. For example, wireless communication system <NUM> can wirelessly communicate with one or more devices directly or via a communication network, such as servers <NUM>-<NUM> over network <NUM>. Wireless communication system <NUM> can use any cellular communication network or a wireless local area network (WLAN), e.g., using WiFi to communicate with another component or system. Wireless communication system <NUM> could communicate directly with a device (e.g., a mobile device of a passenger, a display device, a speaker within vehicle <NUM>), for example, using an infrared link, Bluetooth, etc. User interface system <NUM> may be part of peripheral devices implemented within vehicle <NUM> including, for example, a keyboard, a touch screen display device, a microphone, and a speaker, etc..

Some or all of the functions of ADV <NUM> may be controlled or managed by ADS <NUM>, especially when operating in an autonomous driving mode. ADS <NUM> includes the necessary hardware (e.g., processor(s), memory, storage) and software (e.g., operating system, planning and routing programs) to receive information from sensor system <NUM>, control system <NUM>, wireless communication system <NUM>, and/or user interface system <NUM>, process the received information, plan a route or path from a starting point to a destination point, and then drive vehicle <NUM> based on the planning and control information. Alternatively, ADS <NUM> may be integrated with vehicle control system <NUM>.

For example, a user as a passenger may specify a starting location and a destination of a trip, for example, via a user interface. ADS <NUM> obtains the trip related data. For example, ADS <NUM> may obtain location and route data from an MPOI server, which may be a part of servers <NUM>-<NUM>. The location server provides location services and the MPOI server provides map services and the POIs of certain locations. Alternatively, such location and MPOI information may be cached locally in a persistent storage device of ADS <NUM>.

While ADV <NUM> is moving along the route, ADS <NUM> may also obtain real-time traffic information from a traffic information system or server (TIS). Note that servers <NUM>-<NUM> may be operated by a third party entity. Alternatively, the functionalities of servers <NUM>-<NUM> may be integrated with ADS <NUM>. Based on the real-time traffic information, MPOI information, and location information, as well as real-time local environment data detected or sensed by sensor system <NUM> (e.g., obstacles, objects, nearby vehicles), ADS <NUM> can plan an optimal route and drive vehicle <NUM>, for example, via control system <NUM>, according to the planned route to reach the specified destination safely and efficiently.

Server <NUM> may be a data analytics system to perform data analytics services for a variety of clients. In one embodiment, data analytics system <NUM> includes data collector <NUM> and machine learning engine <NUM>. Data collector <NUM> collects driving statistics <NUM> from a variety of vehicles, either ADVs or regular vehicles driven by human drivers. Driving statistics <NUM> include information indicating the driving commands (e.g., throttle, brake, steering commands) issued and responses of the vehicles (e.g., speeds, accelerations, decelerations, directions) captured by sensors of the vehicles at different points in time. Driving statistics <NUM> may further include information describing the driving environments at different points in time, such as, for example, routes (including starting and destination locations), MPOIs, road conditions, weather conditions, etc..

Based on driving statistics <NUM>, machine learning engine <NUM> generates or trains a set of rules, algorithms, and/or predictive models <NUM> for a variety of purposes. In one embodiment, algorithms <NUM> may include an algorithm or model to detect an obstacle based on sensor data obtained from a plurality of sensors of the ADV, an algorithm or model to predict a distribution of a plurality of positions of the obstacle at a point of time, an algorithm or model to determine a range of positions of the plurality of positions of the obstacle based on a confidence level of the range, an algorithm or model to determine a modified shape with a modified length of the obstacle based on the range of positions of the obstacle, an algorithm or model to plan a trajectory of the ADV based on the modified shape with the modified length of the obstacle, and/or an algorithm or model to control the ADV to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle. Algorithms <NUM> can then be uploaded on ADVs to be utilized during autonomous driving in real-time.

<FIG> and <FIG> are block diagrams illustrating an example of an autonomous driving system used with an ADV according to one embodiment. System <NUM> may be implemented as a part of ADV <NUM> of <FIG> including, but is not limited to, ADS <NUM>, control system <NUM>, and sensor system <NUM>. Referring to <FIG>, ADS <NUM> includes, but is not limited to, localization module <NUM>, perception module <NUM>, prediction module <NUM>, decision module <NUM>, planning module <NUM>, control module <NUM>, routing module <NUM>.

Some or all of modules <NUM>-<NUM> may be implemented in software, hardware, or a combination thereof. For example, these modules may be installed in persistent storage device <NUM>, loaded into memory <NUM>, and executed by one or more processors (not shown). Note that some or all of these modules may be communicatively coupled to or integrated with some or all modules of vehicle control system <NUM> of <FIG>. Some of modules <NUM>-<NUM> may be integrated together as an integrated module.

Localization module <NUM> determines a current location of ADV <NUM> (e.g., leveraging GPS unit <NUM>) and manages any data related to a trip or route of a user. Localization module <NUM> (also referred to as a map and route module) manages any data related to a trip or route of a user. A user may log in and specify a starting location and a destination of a trip, for example, via a user interface. Localization module <NUM> communicates with other components of ADV <NUM>, such as map and route data <NUM>, to obtain the trip related data. For example, localization module <NUM> may obtain location and route data from a location server and a map and POI (MPOI) server. A location server provides location services and an MPOI server provides map services and the POIs of certain locations, which may be cached as part of map and route data <NUM>. While ADV <NUM> is moving along the route, localization module <NUM> may also obtain real-time traffic information from a traffic information system or server.

Based on the sensor data provided by sensor system <NUM> and localization information obtained by localization module <NUM>, a perception of the surrounding environment is determined by perception module <NUM>. The perception information may represent what an ordinary driver would perceive surrounding a vehicle in which the driver is driving. The perception can include the lane configuration, traffic light signals, a relative position of another vehicle, a pedestrian, a building, crosswalk, or other traffic related signs (e.g., stop signs, yield signs), etc., for example, in a form of an object. The lane configuration includes information describing a lane or lanes, such as, for example, a shape of the lane (e.g., straight or curvature), a width of the lane, how many lanes in a road, one-way or two-way lane, merging or splitting lanes, exiting lane, etc..

Perception module <NUM> may include a computer vision system or functionalities of a computer vision system to process and analyze images captured by one or more cameras in order to identify objects and/or features in the environment of the ADV. The objects can include traffic signals, road way boundaries, other vehicles, pedestrians, and/or obstacles, etc. The computer vision system may use an object recognition algorithm, video tracking, and other computer vision techniques. In some embodiments, the computer vision system can map an environment, track objects, and estimate the speed of objects, etc. Perception module <NUM> can also detect objects based on other sensors data provided by other sensors such as a radar and/or LIDAR.

For each of the objects, prediction module <NUM> predicts what the object will behave under the circumstances. The prediction is performed based on the perception data perceiving the driving environment at the point in time in view of a set of map/rout information <NUM>, traffic rules <NUM>, and obstacle position prediction rules/models <NUM>. For example, if the object is a vehicle at an opposing direction and the current driving environment includes an intersection, prediction module <NUM> will predict whether the vehicle will likely move straight forward or make a turn. If the perception data indicates that the intersection has no traffic light, prediction module <NUM> may predict that the vehicle may have to fully stop prior to enter the intersection. If the perception data indicates that the vehicle is currently at a left-turn only lane or a right-turn only lane, prediction module <NUM> may predict that the vehicle will more likely make a left turn or right turn respectively.

For each of the objects, decision module <NUM> makes a decision regarding how to handle the object. For example, for a particular object (e.g., another vehicle in a crossing route) as well as its metadata describing the object (e.g., a speed, direction, turning angle), decision module <NUM> decides how to encounter the object (e.g., overtake, yield, stop, pass). Decision module <NUM> may make such decisions according to a set of rules such as traffic rules or driving rules <NUM>, which may be stored in persistent storage device <NUM>.

Routing module <NUM> is configured to provide one or more routes or paths from a starting point to a destination point. For a given trip from a start location to a destination location, for example, received from a user, routing module <NUM> obtains route and map information <NUM> and determines all possible routes or paths from the starting location to reach the destination location. Routing module <NUM> may generate a reference line in a form of a topographic map for each of the routes it determines from the starting location to reach the destination location. A reference line refers to an ideal route or path without any interference from others such as other vehicles, obstacles, or traffic condition. That is, if there is no other vehicle, pedestrians, or obstacles on the road, an ADV should exactly or closely follows the reference line. The topographic maps are then provided to decision module <NUM> and/or planning module <NUM>. Decision module <NUM> and/or planning module <NUM> examine all of the possible routes to select and modify one of the most optimal routes in view of other data provided by other modules such as traffic conditions from localization module <NUM>, driving environment perceived by perception module <NUM>, and traffic condition predicted by prediction module <NUM>. The actual path or route for controlling the ADV may be close to or different from the reference line provided by routing module <NUM> dependent upon the specific driving environment at the point in time.

Based on a decision for each of the objects perceived, planning module <NUM> plans a path or route for the ADV, as well as driving parameters (e.g., distance, speed, and/or turning angle), using a reference line provided by routing module <NUM> as a basis. That is, for a given object, decision module <NUM> decides what to do with the object, while planning module <NUM> determines how to do it. For example, for a given object, decision module <NUM> may decide to pass the object, while planning module <NUM> may determine whether to pass on the left side or right side of the object. Planning and control data is generated by planning module <NUM> including information describing how vehicle <NUM> would move in a next moving cycle (e.g., next route/path segment). For example, the planning and control data may instruct vehicle <NUM> to move <NUM> meters at a speed of <NUM> miles per hour (mph), then change to a right lane at the speed of <NUM> mph.

Based on the planning and control data, control module <NUM> controls and drives the ADV, by sending proper commands or signals to vehicle control system <NUM>, according to a route or path defined by the planning and control data. The planning and control data include sufficient information to drive the vehicle from a first point to a second point of a route or path using appropriate vehicle settings or driving parameters (e.g., throttle, braking, steering commands) at different points in time along the path or route.

In one embodiment, the planning phase is performed in a number of planning cycles, also referred to as driving cycles, such as, for example, in every time interval of <NUM> milliseconds (ms). For each of the planning cycles or driving cycles, one or more control commands will be issued based on the planning and control data. That is, for every <NUM>, planning module <NUM> plans a next route segment or path segment, for example, including a target position and the time required for the ADV to reach the target position. Alternatively, planning module <NUM> may further specify the specific speed, direction, and/or steering angle, etc. In one embodiment, planning module <NUM> plans a route segment or path segment for the next predetermined period of time such as <NUM> seconds. For each planning cycle, planning module <NUM> plans a target position for the current cycle (e.g., next <NUM> seconds) based on a target position planned in a previous cycle. Control module <NUM> then generates one or more control commands (e.g., throttle, brake, steering control commands) based on the planning and control data of the current cycle.

Note that decision module <NUM> and planning module <NUM> may be integrated as an integrated module. Decision module <NUM>/planning module <NUM> may include a navigation system or functionalities of a navigation system to determine a driving path for the ADV. For example, the navigation system may determine a series of speeds and directional headings to affect movement of the ADV along a path that substantially avoids perceived obstacles while generally advancing the ADV along a roadway-based path leading to an ultimate destination. The destination may be set according to user inputs via user interface system <NUM>. The navigation system may update the driving path dynamically while the ADV is in operation. The navigation system can incorporate data from a GPS system and one or more maps so as to determine the driving path for the ADV.

<FIG> is a block diagram <NUM> illustrating an example of a predication module of an autonomous driving vehicle according to one embodiment. Referring to <FIG>, prediction module <NUM> includes, but is not limited to, feature extractor <NUM>, position distribution module <NUM>, and confidence module <NUM>, which work together using obstacle position prediction rules/models <NUM> to predict a distribution of a plurality of positions of the obstacle at a point of time. Note that modules <NUM>-<NUM> may be integrated into fewer number of modules or a single module.

According to one embodiment, the obstacle such as a vehicle is identified and detected. The obstacle may be detected as a part of perception process performed by perception module <NUM> based on sensor data obtained from various sensors mounted on an ADV such as the sensors as shown in <FIG>. Based on the perception information, feature extractor <NUM> is configured to extract the features of the obstacle. Alternatively, the features of the obstacle may be extracted and provided by perception module <NUM>. An obstacle/object may be a vehicle, motorcycle, bicycle, pedestrian, or animal.

According to one embodiment, position distribution module <NUM> may predict a distribution of a plurality of positions of the obstacle at a point of time.

According to one embodiment, position distribution module <NUM> may predict multiple distributions of a plurality of positions of the obstacle in multiple points of time, each distribution of the plurality of positions of the obstacle corresponding to a distribution of a plurality of positions of the obstacle at one point of time.

According to one embodiment, confidence module <NUM> may determine a confidence level of a range of positions of the plurality of positions of the obstacle at the point of time. The confidence level of a range of positions of the plurality of position at a point of time may correspond to a probability that the obstacle may be within the range of positions at the point of time. For example, the confidence level of the range of positions may be <NUM>%, <NUM>%, <NUM>%, etc..

<FIG> is a block diagram <NUM> illustrating an example of a planning module of an ADV according to one embodiment. Referring to <FIG>, planning module <NUM> includes, but is not limited to, range module <NUM>, shape module <NUM> and trajectory module <NUM>, which work together to plan a trajectory based on a modified shape with a modified length of the ADV. Note that modules <NUM>-<NUM> may be integrated into fewer number of modules or a single module.

According to one embodiment, range module <NUM> may determine a range of positions of the plurality of positions of the obstacle based on a confidence level of the range. For example, range module <NUM> may determine the predicted range of positions of the plurality of positions of the obstacle according to a history of speeds of the obstacle.

According to one embodiment, shape module <NUM> may determine a modified shape with a modified length of the obstacle based on the range of positions of the obstacle. The modified shape with the modified length of the obstacle may include an elongated shape longer than an actual length of the obstacle.

According to one embodiment, trajectory module <NUM> may plan a trajectory of the ADV based on the modified shape with the modified length of the obstacle.

Referring back to <FIG> and <FIG>, control module <NUM> may drive the ADV according to the planned trajectory from trajectory module <NUM> to drive safely to avoid a collision with the obstacle. In this way, the ADV may handle the uncertainty of predicting a future position of the obstacle and the safety of driving may be improved.

<FIG> illustrates an example of planning based on a modified shape of an autonomous driving vehicle according to one embodiment. The ADV <NUM> may drive on a lane <NUM>. The ADV <NUM> may detect an obstacle <NUM> (e.g., a moving vehicle) which may be a leading obstacle driving in front of the ADV <NUM>.

Currently, a prediction module of an ADV only outputs a fixed position of an obstacle based on the highest probability of the speed of the obstacle in a future point of time. However, there may be uncertainty in the prediction of the position of the obstacle <NUM>. For example, the obstacle <NUM> may speed up, or slow down. The prediction of the speed of the obstacle <NUM> may not be so confident. If the trajectory of the ADV <NUM> is planned only according to the fixed position in the future point of time, the ADV <NUM> may not be able to drive safely. As an example, when the obstacle may slow down, the ADV may collide into the obstacle. As another example, when the ADV may try to pass the obstacle from one side, and the obstacle may speed up, the ADV and the obstacle may have a collision.

To address the above problem, the ADV <NUM> may plan a trajectory based on a modified shape of the obstacle to avoid a collision with the obstacle to drive safely. When the prediction is not so confidence about the speed of the obstacle <NUM>, a distribution of possible speeds of the obstacle <NUM> may be predicted. Based the distribution of possible speeds of the obstacle <NUM>, a distribution of possible positions of the obstacle <NUM> may be predicted. The distribution of possible positions may be utilized to determine the region that the obstacle may exist. The modified shape of the obstacle <NUM> may be determined based on the region. The ADV <NUM> may plan the trajectory according to the modified shape of the ADV <NUM>.

As illustrated in <FIG>, the ADV <NUM> may be driving on a lane <NUM>. The perception module of the ADV <NUM> may detect an obstacle <NUM>, e.g., a moving vehicle, which may be a leading obstacle driving on the same lane <NUM>. The prediction module (e.g., <NUM>) of the ADV <NUM> may predict a distribution of possible speeds of the obstacle <NUM>. The distribution of possible speeds of the obstacle <NUM> may be determined according to a history of speeds of the obstacle <NUM>. Based on the distribution of possible speeds of the obstacle <NUM>, the prediction module (e.g., <NUM>) of the ADV <NUM> may predict a list of possible positions of the obstacle <NUM> and predict a distribution of possible positions of the obstacle 602a future point of time T, as illustrated in <FIG>.

<FIG> is a block diagram <NUM> illustrating an example of a distribution of a plurality of positions of the obstacle <NUM> at the point of time T. The X axis represent the position of the obstacle <NUM>, and the Y axis represents the possibility of the obstacle <NUM> at the corresponding position at the point of time T. The possibility of the obstacle <NUM> at the corresponding position at the point of time T may be determined based on a history of the velocity and acceleration of the obstacle <NUM>. For example, the possibility of the obstacle <NUM> at the corresponding position at the point of time T may be determined by using a neutral network, e.g., based on machine learning.

Referring to <FIG> and <FIG>, at the point of time T, the obstacle <NUM> may drive at a typical speed, and arrive at a position L, or the obstacle <NUM> may drive at a fast speed, and arrive at a position L+ d which corresponding to line <NUM>, or the obstacle <NUM> may drive at a slow speed, and arrive at a position L- d which corresponding to line <NUM>, etc. The shaded area <NUM> between line <NUM> and line <NUM> represents a confidence level of the range of positions from position L-d to position L +d. The confidence level of the range of positions may correspond to the probability of the obstacle <NUM> will be positioned within the range.

The ADV <NUM> may set a predetermined confidence level. For example, the confidence level may be <NUM>%, <NUM>%, <NUM>%, <NUM>%, etc. The confidence level may be any value determined by the ADV <NUM>, not being limited to the above examples values. The ADV <NUM> may determine a range of positions of the list of positions of the obstacle based on the predetermined confidence level of the range. As an example, the confidence level of the range may be <NUM>%, the ADV <NUM> may determine the range of positions from position L-d to position L +d have the confidence level of <NUM>%. As illustrated in <FIG>, with <NUM>% confidence, the obstacle <NUM> may be between position L- d <NUM> corresponding to line <NUM> and position L+ d <NUM> corresponding to line <NUM>. The range of positions may have multiple positions including the slowest position <NUM> corresponding to a slowest speed of the obstacle within the confidence level and a fastest position <NUM> of the obstacle within the confidence level.

The ADV <NUM> may determine a modified shape with a modified length of the obstacle <NUM> based on the range of positions of the obstacle. The obstacle <NUM> may be considered to have a modified shape of a region <NUM> based on the range of positions with the confidence level (e.g., <NUM>%). The region <NUM> may extend from a closest bound <NUM> of the slowest position <NUM> in the range of positions to a farthest bound <NUM> of the fastest position <NUM> in the range of positions. The closest bound <NUM> of the slowest position <NUM> may be a back end of the obstacle <NUM> when the obstacle <NUM> is at the position <NUM>. The farthest bound <NUM> of the fastest position <NUM> may be a front end of the obstacle <NUM> when the obstacle <NUM> is at the position <NUM>. A modified length <NUM> of the obstacle <NUM> may be from the closest bound <NUM> of the slowest position <NUM> to the farthest bound <NUM>. As an example, the modified length <NUM> of the obstacle <NUM> may be an original length of the obstacle <NUM> plus the difference between the fastest position <NUM> and the slowest position <NUM>. The modified shape <NUM> with the modified length <NUM> of the obstacle <NUM> may include an elongated shape longer than an actual length of the obstacle. As an example, a modified width of the obstacle <NUM> may be an original width of the obstacle <NUM>. As another example, the modified width of the obstacle <NUM> may be wider than the original width of the obstacle <NUM>.

For different points of time, the ADV <NUM> may predict different distributions of a plurality of positions of the obstacle <NUM>. The ADV <NUM> may predict multiple distributions of a plurality of positions of the obstacle at multiple points of time. Each distribution of the plurality of positions of the obstacle corresponding to a distribution of a plurality of positions of the obstacle at one point of time.

The ADV <NUM> may plan a trajectory based on the modified shape <NUM> with the modified length <NUM> of the obstacle. In one embodiment, the ADV <NUM> may plan to stop before a lower bound <NUM> of the obstacle <NUM> with the modified shape <NUM> and the modified length <NUM>. In one embodiment, the ADV <NUM> may plan to pass the obstacle <NUM> on one side based on an upper bound <NUM> of the obstacle <NUM> with the modified shape <NUM> and the modified length <NUM>.

The ADV <NUM> may constantly update the prediction of the distribution of a plurality of positions of the obstacle <NUM> at a point of time, and update the range of positions of the plurality of positions of the obstacle based on a confidence level of the range, and update the modified shape with the modified length of the obstacle based on the range of positions of the obstacle, and update the planned trajectory accordingly.

In this way, the ADV <NUM> may drive safely according to the planned trajectory to avoid a collision with the obstacle. The uncertainty of predicting a future position of the obstacle may be handled and the safety of driving may be improved. <FIG> is a flow diagram illustrating an example of a process <NUM> for planning based on a modified shape of an autonomous driving vehicle according to one embodiment. Process <NUM> may be performed by processing logic which may include software, hardware, or a combination thereof. For example, process <NUM> may be performed by prediction module <NUM> and planning module <NUM>.

Referring to <FIG>, in operation <NUM>, processing logic detects an obstacle based on sensor data obtained from a plurality of sensors of the ADV. For example, the processing logic may perceive a driving environment surrounding the ADV based on sensor data obtained from various sensors mounted on the ADV (e.g., LIDAR, RADAR, cameras) and generates perception data describing the driving environment. The processing logic may detect the obstacle based on the perception data.

In operation <NUM>, the processing logic predicts a distribution of a plurality of positions of the obstacle at a point of time.

In operation <NUM>, the processing logic determines a range of positions of the plurality of positions of the obstacle based on a confidence level of the range.

In operation <NUM>, the processing logic determines a modified shape with a modified length of the obstacle based on the range of positions of the obstacle.

In operation <NUM>, the processing logic plans a trajectory of the ADV based on the modified shape with the modified length of the obstacle.

In operation <NUM>, the processing logic controls the ADV to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.

Note that some or all of the components as shown and described above may be implemented in software, hardware, or a combination thereof. For example, such components can be implemented as software installed and stored in a persistent storage device, which can be loaded and executed in a memory by a processor (not shown) to carry out the processes or operations described throughout this application. Alternatively, such components can be implemented as executable code programmed or embedded into dedicated hardware such as an integrated circuit (e.g., an application specific IC or ASIC), a digital signal processor (DSP), or a field programmable gate array (FPGA), which can be accessed via a corresponding driver and/or operating system from an application. Furthermore, such components can be implemented as specific hardware logic in a processor or processor core as part of an instruction set accessible by a software component via one or more specific instructions.

Embodiments of the invention also relate to an apparatus for performing the operations herein. Such a computer program is stored in a non-transitory computer readable medium. A machine-readable medium includes any mechanism for storing information in a form readable by a machine (e.g., a computer). For example, a machine-readable (e.g., computer-readable) medium includes a machine (e.g., a computer) readable storage medium (e.g., read only memory ("ROM"), random access memory ("RAM"), magnetic disk storage media, optical storage media, flash memory devices).

The processes or methods depicted in the preceding figures may be performed by processing logic that comprises hardware (e.g. circuitry, dedicated logic, etc.), software (e.g., embodied on a non-transitory computer readable medium), or a combination of both. Although the processes or methods are described above in terms of some sequential operations, it should be appreciated that some of the operations described may be performed in a different order. Moreover, some operations may be performed in parallel rather than sequentially.

Claim 1:
A computer-implemented method (<NUM>) for operating an autonomous driving vehicle (ADV), the method comprising:
detecting (<NUM>) an obstacle based on sensor data obtained from a plurality of sensors of the ADV;
predicting (<NUM>) a distribution of a plurality of positions of the obstacle at a point of time;
determining (<NUM>) a range of positions of the plurality of positions of the obstacle based on a confidence level of the range;
determining (<NUM>) a modified shape with a modified length of the obstacle based on the range of positions of the obstacle;
planning (<NUM>) a trajectory of the ADV based on the modified shape with the modified length of the obstacle; and
controlling (<NUM>) the ADV to drive according to the planned trajectory to drive safely to avoid a collision with the obstacle.