A system includes a processor and a memory. The memory stores program instructions executable by the processor to make a first determination to provide light to operate a vehicle and, based on the first determination, actuate a first set of lights. The memory stores program instructions executable by the processor to make a second determination to provide a communication, and, based on the second determination, actuate a second set of lights to provide the communication.

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.

A vehicle traveling control system according to the example in the present disclosure communicates with an automatic operation control system which drafts a traveling plan of the vehicle, and performs an automatic traveling control for automatically running the vehicle along the traveling plan received from the automatic operation control system. The vehicle traveling control system predicts a risk based on information about surrounding environment of the vehicle, and performs, when the risk is predicted, a risk avoidance control to intervene in the automatic traveling control in order to avoid the risk. When the risk avoidance control is executed, the vehicle traveling control system transmits information on the risk avoidance control to the automatic operation control system.

A method for determining a communications scenario corresponding to an action for producing, by a first movable element situated in a traffic lane, a response to an event. The method includes: determining an event in a vicinity of the first movable element, depending on at least one neighbouring element from a list of neighbouring elements positioned in the vicinity; determining a series of actions able to be performed in response to the event, by consulting a lookup table between at least one event and at least one series of actions; for at least one action of the series of actions, determining a communications scenario associated with the action, the determining a scenario including a sub-step of selecting, in the list of elements, for at least one communications scenario message, at least one neighbouring element receiving the message.

Techniques to predict object behavior in an environment are discussed herein. For example, such techniques may include determining a trajectory of the object, determining an intent of the trajectory, and sending the trajectory and the intent to a vehicle computing system to control an autonomous vehicle. The vehicle computing system may implement a machine learned model to process data such as sensor data and map data. The machine learned model can associate different intentions of an object in an environment with different trajectories. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on object's intentions and trajectories.

Provided is a communication apparatus including: a target specification unit for specifying a position of a target at risk of approaching a moving body; and a communication unit for transmitting, based on the target position specified by the target specification unit, warning information including positional information of an external terminal to output a warning; a program causing a computer to function as: a target specification unit for specifying a position of a target at risk of approaching a moving body; and a communication unit for transmitting, based on the target position specified by the target specification unit, warning information including positional information of an external terminal to output a warning; and a communication method including: specifying a position of a target at risk of approaching a moving body; and transmitting, based on the specified target position, warning information including positional information of an external terminal to output a warning.

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.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

A cooperative intelligent traffic system is provided for use between bicycles, motorcycles, and other vehicles. Sensor data from mobile devices and other sensor devices associated with the vehicle can be sent to an edge network computing device (e.g., a multi-access edge computing device) and be processed at the edge network to identify threats and hazards, and then transmit the threat assessment data to other bicycles, motorcycles, and vehicles nearby. The threat assessment data can be used by the operators of the other vehicles to warn them of upcoming threats, hazards, road conditions, and other pertinent conditions.