Techniques for determining a warped occupancy grid fit to a vehicle trajectory are discussed herein. In some examples, a portion of memory may be allocated to an occupancy grid. Further, a warped occupancy grid can be warped and associated with an environment that an autonomous vehicle is traversing according to a trajectory and/or throughway. A transformation maybe be determined between the warped occupancy grid and the memory allocated to the occupancy grid. Sensor data can be received from a sensor associated with the autonomous vehicle and may be associated with the warped occupancy grid and stored in the occupancy grid. The autonomous vehicle may be controlled according to the warped occupancy grid by identifying sensor data returns in cells of the warped occupancy grid that may indicate a detection of an object in a path of travel of the vehicle.

Motion control systems and methods are provided in a vehicle. In one embodiment, a motion control system includes a controller. The controller is configured to: receive target trajectory data associated with an upcoming trajectory of the autonomous vehicle; determine a yaw rate reference and a relative heading reference associated with the upcoming target trajectory based on a numerical integration of the target trajectory data; and control a trajectory of the autonomous vehicle based on the yaw rate reference and the relative heading reference.

A method for determining the values of parameters for a controller of a vehicle, wherein respective error measures are calculated for different sets of values and a set of values is selected based on the error measures.

Systems and methods are disclosed for sensor-based in-vehicle dynamic driver gaze tracking. In one implementation, one or more first inputs can be received. The first input(s) can be processed to compute one or more directional adjustments. Second input(s) originating from an image sensor positioned within a vehicle can be received. The second input(s) can be processed to determine a gaze direction of at least one eye of a driver of the vehicle. Based on the determined gaze direction of the at least one eye of the driver and the computed first directional adjustment, aspect(s) of distractedness of the driver can be determined. Operation(s) can be initiated based on the determined aspect(s) of distractedness.

To effectively prevent a driver from being annoyed, provided is a driving assistance device (1) including: a target acquisition unit (30) configured to acquire a target existing in a periphery of an own vehicle; and a control unit (10) configured to execute, when the target acquired by the target acquisition unit (30) is determined as an object to be avoided which is highly likely to collide with the own vehicle, collision avoidance control of controlling travel of the own vehicle such that the collision between the own vehicle and the object to be avoided is avoided. The control unit (10) is configured to suppress the collision avoidance control when a specific target (ST) is detected between the own vehicle and the object to be avoided, and a positional relationship among the specific target (ST), the object to be avoided, and the own vehicle satisfies a predetermined condition.

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.

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 that no train is approaching the railroad crossing from the sensor data. The control device detects an indication of a stopped vehicle in front of the autonomous vehicle and behind the railroad crossing. The control device determines whether the stopped vehicle is associated with a mandatory stop rule. The mandatory stop rule indicates vehicles carrying hazardous materials have to stop behind the railroad crossing even when no train is approaching or traveling through the railroad crossing. If it is determined that the stopped vehicle is associated with the mandatory stop rule, the control device waits behind the stopped vehicle until the stopped vehicle crosses the railroad crossing and instructs the autonomous vehicle to cross the railroad.

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 that the railroad crossing is closed from the sensor data. The control device determines one or more re-routing options from map data. The control device determines whether at least one re-routing option reaches a predetermined destination of the autonomous vehicle. In response to determining that the at least one re-routing option reaches the predetermined destination, the control device selects a particular re-routing option based at least on determining that the autonomous vehicle is able to travel according to the particular re-routing option autonomously. The control device instructs the autonomous vehicle to re-route according to the particular re-routing option.