Systems and methods are proposed for coordinating and providing assistive traction drive forces during towing events between a motor vehicle and one or more charging trailers. The assistive traction drive forces may be provided in the form of a propulsive torque applied by an electric machine of the charging trailer. Electrical energy for powering the electric machine may be supplied by a powertrain system of the towing vehicle or an energy storage device of an electrified recreational/industrial vehicle that is operably connected to the charging trailer. Energy expended by the energy storage device for powering the electric machine may be replenished during the towing event by regenerative braking.

A parking assist system is configured to autonomously move a vehicle to a target parking position. The parking assist system includes: a distance acquiring unit configured to acquire a distance between the vehicle and a vehicle stopper provided in the target parking position; and a control unit configured to control a movement of the vehicle to the target parking position based on the distance acquired by the distance acquiring unit.

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.

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 a train is approaching the railroad crossing and waits for the train to pass the railroad crossing. The control device determines that the train has passed the railroad crossing. The control device instructs the autonomous vehicle to cross the railroad.

The present disclosure in some embodiments provides a method and apparatus which utilize a learning model based on deep learning for enabling a vehicle to autonomously estimate a road surface condition by using a deep learning-based learning model, determine a terrain mode optimized for the road surface being traveled by the vehicle, and control respective in-vehicle modules and thereby automatically control the terrain mode.

A method for assignment of vehicle control includes receiving route data indicating a route between a starting location of a vehicle and a destination location, and determining an optimal vehicle configuration for the route based on a target vehicle speed and a hybrid torque split. The method further includes receiving a driver requested torque value and determining a passive pedal torque value based on the route data and vehicle powertrain data. The method further includes selectively assigning control of the vehicle to a vehicle system or to a driver of the vehicle based on the driver requested torque value and the passive pedal torque value.