Baidu USA LLC

Yajia Zhang

Kecheng Xu

Jiangtao Hu

Liangliang Zhang

Yifei Jiang

Haoyang Fan

Qi Luo

Jinghao Miao

Dong Li

Qi Kong

Weicheng Zhu

Jiacheng Pan

Jingao Wang

Jiaming Tao

Hongyi Sun

Fan Zhu

Guang Yu Yang

Liyun Li

Xiang Yu

Sen Hu

Li Zhuang

According to one embodiment, during a first planning cycle, a first lane boundary of a driving environment perceived by an ADV is determined using a first lane boundary determination scheme (e.g., current lane boundary), which has been designated as a current lane boundary determination scheme. A first trajectory is planned based on the first lane boundary to drive the ADV to navigate through the driving environment. The first trajectory is evaluated against a predetermined set of safety rules (e.g., whether it will collide or get too close to an object) to avoid a collision with an object detected in the driving environment. In response to determining that the first trajectory fails to satisfy the safety rules, a second lane determination boundary of the driving environment is determined using a second lane boundary determination scheme and a second trajectory is planned based on the second lane boundary to drive the ADV.

Hierarchical path decision system for planning a path for an autonomous driving vehicle

In one embodiment, an autonomous driving system of an ADV perceives a driving environment surrounding the ADV based on sensor data obtained from various sensors, including detecting one or more lanes and at least a moving obstacle or moving object. For each of the lanes identified, an NN lane feature encoder is applied to the lane information of the lane to extract a set of lane features. For a given moving obstacle, an NN obstacle feature encoder is applied to the obstacle information of the obstacle to extract a set of obstacle features. Thereafter, a lane selection predictive model is applied to the lane features of each lane and the obstacle features of the moving obstacle to predict which of the lanes the moving obstacle intends to select.

Neural network with lane aggregation for lane selection prediction of moving objects during autonomous driving

In one embodiment, a method is provided. The method includes determining a first reference line representing a path through an environment for an autonomous driving vehicle. The method also includes determining a speed constraint function based on a set of speed limits associated with the environment, a set of curvatures of the path, and a set of obstacles in the environment, wherein the speed constraint function comprises a continuous function. The method further includes determining a set of speeds for the path through the environment based on the speed constraint function. The method further includes controlling the autonomous driving vehicle based on the path and the set of speeds.

Determining speeds for an autonomous vehicle

According to one embodiment, an obstacle is predicted to move from a starting point to an end point based on perception data perceiving a driving environment surrounding an ADV that is driving within a lane. A longitudinal movement trajectory from the starting point to the end point is generated in view of a shape of the lane. A lateral movement trajectory from the starting point to the end point is generated, including optimizing a shape of the lateral movement trajectory using a first polynomial function. The longitudinal movement trajectory and the lateral movement trajectory are then combined to form a final predicted trajectory that predicts how the obstacle is to move. A path is generated to control the ADV to move in view of the predicted trajectory of the obstacle, for example, to avoid the collision with the obstacle.

Method for generating prediction trajectories of obstacles for autonomous driving vehicles

Via a first processing thread, an ADV is controlled according to a first trajectory that was generated based on a first reference line starting at a first location. Concurrently via a second processing thread, a second reference line is generated based on a second location of the first trajectory that the ADV will likely reach within a predetermined period of time in future. The predetermined period of time is greater than or equals to an amount of time to generate a reference line for the ADV. The second reference line is generated while the ADV is moving according to the first trajectory and before reaching the second location. Subsequently, in response to determining that the ADV is within a predetermined proximity of the second location, a second trajectory is generated based on the second reference line without having to calculate the second reference line at the second location.

Method and system for generating reference lines for autonomous driving vehicles using multiple threads

In response to perceiving a moving object, one or more possible object paths of the moving object are determined based on the prior movement predictions of the moving object, for example, using a machine-learning model, which may be created based on a large amount of driving statistics of different vehicles. For each of the possible object paths, a set of trajectory candidates is generated based on a set of predetermined accelerations. Each of the trajectory candidates corresponds to one of the predetermined accelerations. A trajectory cost is calculated for each of the trajectory candidates using a predetermined cost function. One of the trajectory candidates having the lowest trajectory cost amongst the trajectory candidates is selected. An ADV path is planned to navigate the ADV to avoid collision with the moving object based on the lowest costs of the possible object paths of the moving object.

Method for predicting movement of moving objects relative to an autonomous driving vehicle

In one embodiment, a method of generating a path for an autonomous driving vehicle (ADV) is disclosed. The method includes obtaining vehicle configuration of the ADV. The vehicle configuration includes a longitudinal state of the ADV and a lateral state of the ADV relative to a discretized point along a reference line. The method further includes estimating one or more boundaries for the lateral state of the ADV with respect to the longitudinal state of the ADV. The estimation of the boundaries includes obtaining vehicle parameters and sensor data of the ADV, using a vehicle kinematic model to estimate the boundaries based on the vehicle parameters and the sensor data, and outputting the estimated boundaries. The method further includes generating an optimal path to control the ADV based on the vehicle configuration and the estimated boundaries.

Variable boundary estimation for path planning for autonomous driving vehicles

Methods and systems to predict object movement within a driving environment is disclosed. In one embodiment, one or more objects are detected within the driving environment. One or more predicted trajectories are computed for each of the objects based on map and route information to produce a set of predicted trajectories for the objects. The set of predicted trajectories is used to enumerate a number of combinations of predicted trajectories on which the objects possibly travel within the driving environment. A risk value is computed for each of the combinations to generate a number of corresponding risk values. An autonomous vehicle is controlled based on a combination having a lowest risk value included in the corresponding risk values.

Methods and systems to predict object movement for autonomous driving vehicles

A moving object such as a vehicle is identified within an intersection having multiple exits. The moving object and the intersection and its exits may be identified based on sensor data obtained from various sensors mounted on an ADV. An exit coordinate map is generated based on the orientation of the moving object and a relative position of each of the exits of the intersection with respect to the current position of the moving object. For each of the exits, an exit probability of the exit that the moving object likely exits the intersection using the exit coordinate map. Thereafter, a trajectory of the ADV is planned to navigate through the intersection to avoid the collision with the moving object based on the exit probabilities of the exits of the intersection. The above process is iteratively performed for each of the moving objects detected within the proximity of the intersection.

Method for determining exiting intersection of moving objects for autonomous driving vehicles

A parking system for autonomous driving vehicles optimizes a solution to a parking problem. The ADV detects a parking lot and selects a parking space. The ADV defines constraints for the parking lot, parking space, and kinematic constraints of the ADV, and generates a plurality of potential parking paths to the parking space, taking into account the constraints of the parking lot, parking space, and kinematics of the ADV, but without taking into any obstacles that may be surrounding the ADV. The ADV determines a cost for traversing each of the parking paths. One or more least cost candidate paths are selected from the parking paths, then one or more candidate paths are eliminated based on obstacles surrounding the ADV. Remaining candidates can be analyzed using a quadratic optimization system. A best parking path can be selected from the remaining candidates to navigate the ADV to the parking space.

Planning parking trajectory generation for self-driving vehicles using optimization method

A method of generating a path for an autonomous driving vehicle (ADV) includes obtaining a plurality of path inputs including a lateral and a longitudinal starting state, a threshold lateral jerk, and a set of static obstacle boundaries with respect to a reference line, obtaining a plurality of path constraints related to the threshold lateral jerk, avoidance of static obstacles, and a threshold lateral velocity, obtaining a cost function associated with a path objective, the cost function comprising first, second, and third terms relating to cumulative lateral distances, to cumulative first order lateral rates of change, and to cumulative second order lateral rates of change, respectively, generating a plurality of planned ADV states as path results based on the plurality of path inputs, the plurality of path constraints, and the cost function and generating control signals to control the ADV based on the plurality of planned ADV states.

Path generation for static obstacle avoidance

Methods and systems for multimodal motion planning framework for autonomous driving vehicles are disclosed. In one embodiment, driving environment data of an autonomous vehicle is received, where the environment data includes a route segment. The route segment is segmented into a number of route sub-segments. A specific driving scenario is assigned to each of the route sub-segments, where each specific driving scenario is included in a set of driving scenarios. A first motion planning algorithm is assigned according to a first assigned driving scenario included in the set of driving scenarios. The first motion planning algorithm is invoked to generate a first set of trajectories. The autonomous vehicle is controlled based on the first set of trajectories.

Multimodal motion planning framework for autonomous driving vehicles

A method, apparatus, and system for generating an optimal path for an autonomous driving vehicle (ADV) are disclosed. The method includes receiving optimization inputs comprising an ADV starting state, a maximal lateral jerk, and static obstacle boundaries with respect to a reference line; receiving optimization constraints comprising constraints relating to the maximal lateral jerk and avoidance of one or more static obstacles; receiving a cost function associated with an optimization objective, the cost function comprising a first term relating to cumulative lateral distances, a second term relating to cumulative first order lateral rates of change, and a third term relating to cumulative second order lateral rates of change; generating planned ADV states as optimization results with nonlinear optimization, by minimizing a value of the cost function; and generating control signals to control the ADV based on the plurality of planned ADV states.

Optimal path generation for static obstacle avoidance

In one embodiment, a system generates a plurality of trajectory candidates for an autonomous driving vehicle (ADV) from a starting point to an end point of a particular driving scenario. The system generates a reference trajectory corresponding to the driving scenario based on a current state of the ADV associated with the starting point and an end state of the ADV associated with the end point, where the reference trajectory is associated with an objective. For each of the trajectory candidates, the system compares the trajectory candidate with the reference trajectory to generate an objective cost representing a similarity between the trajectory candidate and the reference trajectory. The system selects one of the trajectory candidates as a target trajectory for driving the ADV based on objective costs of the trajectory candidates.

Method to evaluate trajectory candidates for autonomous driving vehicles (ADVs)

According to one embodiment, an ADV is controlled according to a first trajectory planned during a first planning cycle. A control error is determined which represents a drifting error at a first location of the ADV at a first point in time at the end of the first planning cycle. A second point in time is selected on the first trajectory. A second trajectory is generated from a second location on the first trajectory corresponding o the second point in time as a starting location of the second trajectory for a second planning cycle as a next planning cycle. A segment of the first trajectory between the first point in time and the second point in time is combined with the second trajectory to generate a third trajectory for the second planning cycle. The ADV is driven and controlled according to the third trajectory corresponding to the second planning cycle.

Growing community of inventors

Sunnyvale, CA, United States of America

Yajia Zhang