Provided is an obstacle avoidance apparatus that can specify a distance between a subject vehicle and an obstacle when making the subject vehicle avoid the obstacle. An obstacle avoidance apparatus includes: an obstacle movement predictor that predicts movement of the obstacle; and a constraint generator that establishes a constraint on a state quantity or a control input of the subject vehicle by determining whether to steer right or left around the obstacle and defining a no-entry zone for preventing the subject vehicle from colliding with the obstacle. The constraint generator incorporates, into the no-entry zone, an area to the left of the obstacle when determining to steer right around the obstacle, and incorporates, into the no-entry zone, an area to the right of the obstacle when determining to steer left around the obstacle.

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.

Aspects of the technology relate to exception handling for a vehicle. For instance, a current trajectory for the vehicle and sensor data corresponding to one or more objects may be received. Based on the received sensor data, projected trajectories of the one or more objects may be determined. Potential collisions with the one or more objects may be determined based on the projected trajectories and the current trajectory. One of the potential collisions that is earliest in time may be identified. Based on the one of the potential collisions, a safety-time-horizon (STH) may be identified. When a runtime exception occurs, before performing a precautionary maneuver to avoid a collision, waiting no longer than the STH for the runtime exception to resolve.

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

Among other things, equipment is located at an intersection of a transportation network. The equipment includes an input to receive data from a sensor oriented to monitor ground transportation entities at or near the intersection. A wireless communication device sends to a device of one of the ground transportation entities, a warning about a dangerous situation at or near the intersection, there is a processor and a storage for instructions executable by the processor to perform actions including the following. A machine learning model is stored that can predict behavior of ground transportation entities at or near the intersection at a current time. The machine learning model is based on training data about previous motion and related behavior of ground transportation entities at or near the intersection. Current motion data received from the sensor about ground transportation entities at or near the intersection is applied to the machine learning model to predict imminent behaviors of the ground transportation entities. An imminent dangerous situation for one or more of the ground transportation entities at or near the intersection is inferred from the predicted imminent behaviors. The wireless communication device sends the warning about the dangerous situation to the device of one of the ground transportation entities.

The disclosure relates to a method for preventing a collision of a vehicle with another road user carrying a mobile electronic device. The method comprises receiving identification data from the mobile electronic device at a receiving unit of the vehicle. Thereafter, a position of the mobile electronic device is calculated based on a signal strength of the signal carrying the received identification data. Subsequently, a collision risk of the road user carrying the mobile electronic device and the vehicle is determined. If a collision risk is determined, a warning activity for a user of the vehicle is triggered. The disclosure additionally relates to a collision warning system comprising a receiving unit for receiving identification data from a mobile electronic device and a data processing device. The data processing device is communicatively coupled to the receiving unit. Moreover, the data processing device comprises means for calculating a position of the mobile electronic device, for determining the collision risk, and for triggering a warning activity for a user of the vehicle. Furthermore, a vehicle having such a collision warning system is presented.

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.

Methods and systems are disclosed for cross-validating a second sensor with a first sensor. Cross-validating the second sensor may include obtaining sensor readings from the first sensor and comparing the sensor readings from the first sensor with sensor readings obtained from the second sensor. In particular, the comparison of the sensor readings may include comparing state information about a vehicle detected by the first sensor and the second sensor. In addition, comparing the sensor readings may include obtaining a first image from the first sensor, obtaining a second image from the second sensor, and then comparing various characteristics of the images. One characteristic that may be compared are object labels applied to the vehicle detected by the first and second sensor. The first and second sensors may be different types of sensors.

At least a method for determining risk behavior of a driver is described. While a vehicle is being driven, data is obtained related to the position and movement of a wireless communications device. The data may indicate the type of behavior exhibited by the driver while the vehicle is being driven.

A system includes a first-vehicle processor configured to receive a signal broadcast from a second vehicle. The processor is also configured to determine a distance between a first transceiver, receiving the signal, and a second transceiver, transmitting the signal. The processor is further configured to determine second vehicle dimensions. Also, the processor is configured to digitally map a second vehicle perimeter around a second transceiver location, determined based on the distance and alert a first vehicle driver of a likely overlap condition of the second vehicle perimeter and a first vehicle perimeter.