Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

A vehicle operable by an unrestrained or uncontained rider and including a controller programmed to identify a trigger for an autonomous vehicle response. A sensor of the vehicle is in communication with the controller and operable to detect a predefined condition as the trigger. A rider sensor system in communication with the controller includes one or both of: a rider cognition sensor, and a rider physical sensor to detect physical engagement between rider and vehicle. On the condition of the controller determining from the rider sensor system that there is positive rider engagement, the controller is programmed to instruct a first level of autonomous vehicle response to the one or more actuators to effect a change in the operation of the vehicle in response to identification of the trigger. In the absence of positive rider engagement determined by the controller, the first level of autonomous vehicle response is prohibited.

A vehicle operable by an unrestrained or uncontained rider and including a controller programmed to identify a trigger for an autonomous vehicle response. A sensor of the vehicle is in communication with the controller and operable to detect a predefined condition as the trigger. A rider sensor system in communication with the controller includes one or both of: a rider cognition sensor, and a rider physical sensor to detect physical engagement between rider and vehicle. On the condition of the controller determining from the rider sensor system that there is positive rider engagement, the controller is programmed to instruct a first level of autonomous vehicle response to the one or more actuators to effect a change in the operation of the vehicle in response to identification of the trigger. In the absence of positive rider engagement determined by the controller, the first level of autonomous vehicle response is prohibited.

Systems and methods for self-driving collision prevention are presented. The system comprises a self-driving vehicle safety system, having one or more sensors in communication with a control system. The control system is configured determine safety fields and instruct the sensors to scan a region corresponding to the safety fields. The control system determines exclusion regions, and omits the exclusion regions from the safety field. The safety system may also include capability reduction parameters that can be used to constrain the drive system of the vehicle, for example, by restricting turning radius and speed in accordance with the safety fields.

Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

The present disclosure relates generally to autonomous vehicles, and more specifically to techniques for representing trajectories of objects such as traffic participants (e.g., vehicles, pedestrians, cyclists) in a computationally efficient manner (e.g., for multi-object tracking by autonomous vehicles). An exemplary method for generating a control signal for controlling a vehicle includes: obtaining a parametric representation of a trajectory of a single object in the same environment as the vehicle; updating the parametric representation of the single-object trajectory based on data received by one or more sensors of the vehicle within a framework of multi-object and multi-hypothesis tracker; and generating the control signal for controlling the vehicle based on the updated trajectory of the object.

A method for exchanging pieces of maneuver information between vehicles. A parameterizable third-party trajectory planner provided by a third-party vehicle and mapping future pieces of maneuver information of the third-party vehicle are parameterized and executed in an ego vehicle, using at least one time parameter, to obtain at least one future third-party trajectory of the third-party vehicle.

A method for exchanging pieces of maneuver information between vehicles. A parameterizable third-party trajectory planner provided by a third-party vehicle and mapping future pieces of maneuver information of the third-party vehicle are parameterized and executed in an ego vehicle, using at least one time parameter, to obtain at least one future third-party trajectory of the third-party vehicle.

A system for an autonomous vehicle that predicts a location-based maneuver of a remote vehicle located in a surrounding environment includes one or more vehicle sensors collecting sensory data indicative of one or more vehicles located in the surrounding environment. The system also includes one or more automated driving controllers in electronic communication with the one or more vehicle sensors. The one or more automated driving controllers execute instructions to compare a lane of travel of the remote vehicle with a current lane of travel of the autonomous vehicle. In response to determining the lane of travel of the remote vehicle is a different lane than the current lane of the autonomous vehicle, the one or more automated driving controllers predict the location-based maneuver of the remote vehicle based on aggregated vehicle metrics that are based on historical data collected at the specific geographical location.

An object detection device includes first and second detectors each configured to detect an object by transmitting an ultrasonic wave in a moving direction of the moving object and receiving a reflected wave of the ultrasonic wave, a second detector, a memory, and a hardware processor coupled to the memory, and configured to: determine that an obstacle is present in the moving direction of the moving object, based on object detection results by the first and second detectors; determine crossing of the obstacle based on object detection results by the first and second detectors in a state in which it is being determined that the obstacle is present; and cause a driving controller mounted on the moving object, to release driving restriction control of restricting movement of the moving object when determining the crossing, or prohibit the driving controller from releasing the driving restriction control under a predetermined condition.