One general aspect includes a system to prevent a boxed-in driving situation, the system including: a memory configured to include one or more executable instructions and a processor configured to execute the executable instructions, where the executable instructions enable the processor to detect a preventable box-in driving situation; and, based on the detection of the preventable box-in driving situation, adjust a location of a trigger threshold along a vehicle path.

A system receives confirmation that a vehicle has accepted automatic control imposition for a drive within a geo-fenced boundary. The system tracks travel of a plurality of vehicles, including the vehicle, within the geo-fenced boundary. The system may determine that the vehicle has a threshold likelihood of encountering at least one of another vehicle or a boundary of the geo-fence at a threshold speed or above and responsive to the determination, impose automatic control on the vehicle, including at least one of controlled braking or speed limiting.

Various embodiments for systems and methods for cooperative driving of connected autonomous vehicles using responsibility-sensitive safety (RSS) rules are disclosed herein. The CAV system integrates proposed RSS rules with CAV's motion planning algorithm to enable cooperative driving of CAVs. The CAV system further integrates a deadlock detection and resolution system for resolving traffic deadlocks between CAVs. The CAV system reduces redundant calculation of dependency graphs.

A system and method for providing probabilistic-based lane-change decision making and motion planning that include receiving data associated with a roadway environment of an ego vehicle. The system and method also include performing gap analysis to determine at least one gap between neighboring vehicles that are traveling within the target lane to filter out an optimal merging entrance for the ego vehicle to merge into the target lane and determining a probability value associated with an intention of a driver of a following neighboring vehicle to yield to allow the ego vehicle to merge into the target lane. The system and method further include controlling the ego vehicle to autonomously continue traveling within the current lane or autonomously merge from current lane to the target lane based on at least one of: if the optimal merging entrance is filtered out and if the probability value indicates an intention of the driver to yield.

The present disclosure provides a vehicle behavior prediction method and apparatus, an electronic device, and a computer-readable storage medium, which relates to the field of intelligent driving. The method includes: determining a target traffic light corresponding to t a target vehicle a current intersection; determining a neighboring traffic light corresponding to the target traffic light and the indication status of the neighboring traffic light according to the target traffic light, the indication status of the target traffic light and a traffic light status mapping relationship table; acquiring a position of an obstacle vehicle and predicting each possible travel path of the obstacle vehicle, and determining a traffic light corresponding to each possible travel path; and determining a final possible travel path of the obstacle vehicle according to each possible travel path and the indication status of the traffic light corresponding to each possible travel path.

A method includes identifying an object that is invading a lane that an autonomous vehicle is occupying, and generating a constraint about a point of crossing, where the constraint has a direction and a length, and the point of crossing represents a location of where the object and the autonomous vehicle will collide if the object maintains its current trajectory and the autonomous vehicle maintains its current trajectory. The method includes applying the constraint to a motion plan associated with the autonomous vehicle, and issuing one or more commands to adjust movement of the autonomous vehicle in response to encountering the constraint.

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

A method of operating a vehicle, comprises determining, by a computer located in a first vehicle, that an initial distance between the first vehicle and a second vehicle is less than a first distance or a second distance, where the second vehicle is located in a same lane as and in front of the first vehicle; generating, in response to the determining, a sequence of position values and velocity values for the first vehicle, wherein each of the position values and each of the velocity values are associated with a time value; and causing the first vehicle to increase a distance between the first vehicle and the second vehicle by causing the first vehicle to move or operate according to the sequence of position values and velocity values.

Systems and methods of processing crowdsourced navigation information for use in autonomous vehicle navigation are disclosed. A method may include processing, by a mapping server, crowdsourced navigation information from a plurality of vehicles obtained by sensors coupled to the plurality of vehicles, wherein the navigation information describes road lanes of a road segment; collecting data about landmarks identified proximate to the road segment, the landmarking including a traffic sign; generating, by the mapping server, an autonomous vehicle map for the road segment, wherein the autonomous vehicle map includes a spline corresponding to a lane in the road segment and the landmarks identified proximate to the road segment; and distributing, by the mapping server, the autonomous vehicle map to an autonomous vehicle for use in autonomous navigation over the road segment.

A motion planner performs motion planning with collision assessment, using a motion planning lattice that represents configuration states of a primary agent (e.g., autonomous vehicle) as nodes and transitions between states as edges. The system may assign cost values to edges, the cost values representing probability or likelihood of collision for the corresponding transition. The cost values may additionally or alternatively represent a severity of collision, for example generated via a parametric function with two or more parameters and one or more weights. A primary agent and/or dynamic obstacles may be represented as respective oriented bounding boxes. Some obstacles (e.g., road markings, edge of road) may be represented as curves. A trajectory of a primary agent and/or dynamic obstacle may be represented by respective sets of fitted polynomial functions, edges on the planning graph, which represent transitions in states of the primary agent, the system sets value representing a probability of collision, and optionally representing a severity of the collision. The system then causes the actuator system of the primary agent to implement a motion plan with the applicable identified path based at least in part on the optimization.