Techniques for predicting safety metrics associated with near-miss conditions for a vehicle, such as an autonomous vehicle, are discussed herein. For instance, a training system identifies an object in an environment and determines a trajectory for the object. The training system may receive a trajectory for a vehicle and associate the trajectory for the object and the trajectory for the vehicle with an event involving the object and the vehicle. In examples, the training system determines a parameter associated with motion of the vehicle as indicated by the trajectory of the vehicle relative to the trajectory of the object, and the event. Then, the training system may determine a safety metric associated with the event that indicates whether the vehicle came within a threshold of a collision with the object during a time period associated with the event.

Techniques are discussed herein for generating, evaluating, and determining trajectories for autonomous vehicles traversing environments. A state transition model may be generated and used to determine a trajectory from multiple possible trajectories generated by one or more vehicle systems. In some examples, a state transition model may determine a trajectory based on the validation results of the possible trajectories, along with vehicle status data from one or more vehicle components. Various techniques described herein may improve vehicle safety and driving efficiency, by ensuring that the vehicle determines a safe and valid trajectory consistently while navigating the environment, while also being responsive to requests and status updates from various vehicle components.

A location of an occupant within a vehicle and/or an activity engaged in by the occupant may be determined. Based on the location and/or the activity, a point of interest associated with the occupant and/or the vehicle may be determined. One or more systems of the vehicle, such as the steering and/or suspension, may be controlled to minimize acceleration associated with the point of interest, thereby increasing a comfort of the occupant. In instances where the vehicle includes more than one occupant, the vehicle may be adjusted to accommodate the multiple occupants.

The present disclosure relates to a vehicle, a method for the vehicle, and a system for the vehicle and is directed to calculate an avoidance trajectory more simply and quickly in calculating the avoidance trajectory for avoiding a collision with nearby another vehicle. To this end, the system disclosed herein includes a detection device provided in a host vehicle to detect a position, a direction, and a speed of the other vehicle around the host vehicle, and a controller provided in the host vehicle and configured to predict a possibility of a collision between the host vehicle and the other vehicle based on the position, the direction, and the speed of the other vehicle detected by the detection device, and calculate an avoidance trajectory for the host vehicle to avoid the collision with the other vehicle based on the possibility of the collision. The controller calculates the avoidance trajectory after replacing an actual curved road with a virtual straight road when calculating the avoidance trajectory.

Performance anomalies in autonomous vehicle can be difficult to identify, and the impact of such anomalies on systems within the autonomous vehicle may be difficult to understand. In examples, systems of the autonomous vehicle are modeled as nodes in a probabilistic graphical network. Probabilities of data generated at each of the nodes is determined. The probabilities are used to determine capabilities associated with higher level functions of the autonomous vehicle.

Systems and methods for controlling an autonomous vehicle are described. A trajectory planner module provides a first trajectory to a trajectory control module. The trajectory control module determines parameters of the first trajectory. The trajectory control module compares the parameters to a respective threshold value. The trajectory control module obtains one or more alternative trajectories, determines parameters of each alternative trajectory, and compares the parameters of the alternative trajectory to a respective threshold value. The trajectory control module selects a trajectory for controlling the autonomous vehicle that has parameters which are within a range defined by the threshold values and controls the autonomous vehicle based on the selected trajectory. Thus, before handing back control to a driver, the trajectory control module selects from alternate trajectories for controlling the autonomous vehicle.

A vehicle includes an engine, an accelerator pedal, and a controller. The controller is programmed to command torque to the engine based on a set speed of adaptive cruise control and is programmed to, in response to the adaptive cruise control being active, a measured lateral acceleration of the vehicle exceeding a user-defined lateral acceleration threshold during a road curve, and the accelerator pedal being released, reduce a speed of the vehicle below the set speed until the measured lateral acceleration is less than the lateral acceleration threshold.

Systems and methods for managing environmental conditions for an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes a perception sensor configured to generate perception data indicative of a condition of the environment, a network communication transceiver configured to communicate with an oversight system and an external weather condition source, a non-transitory computer readable medium, and a processor. The processor is configured to: receive the perception data from the at least one perception sensor, receive an indication of current weather conditions from the external weather condition source, determine a current environmental condition severity level from a plurality of severity levels based on the perception data and the indication of current weather conditions, modify one or more driving parameters that that govern a range of actions that can be autonomously executed by the autonomous vehicle, and navigate the autonomous vehicle based on the modified driving parameters.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

A control device associated with an autonomous vehicle receives sensor data and detects that the autonomous vehicle is approaching a railroad crossing based on the sensor data. The control device determines a target lane to travel while crossing the railroad. The target lane is a lane that has available space with at least a length of the autonomous vehicle on the other side of the railroad as opposed to a side of the railroad where the autonomous vehicle is currently traveling. The control device instructs the autonomous vehicle to travel on the target lane. The control device determines that no train is approaching the railroad crossing. The control device instructs the autonomous vehicle to cross the railroad if the target lane still provides available space with at least the length of the autonomous vehicle on the other side of the railroad.