Aspects of the disclosure relate to an autonomous vehicle that may detect other nearby vehicles and designate stationary vehicles as being in one of a short-term stationary state or a long-term stationary state. This determination may be made based on various indicia, including visible indicia displayed by the detected vehicle and traffic control factors relating to the detected vehicle. For example, the autonomous vehicle may identify a detected vehicle as being in a long-term stationary state based on detection of hazard lights being displayed by the detected vehicle, as well as the absence of brake lights being displayed by the detected vehicle. The autonomous vehicle may then base its control strategy on the stationary state of the detected vehicle.

A method and a device for operating an assistance system of a vehicle involves detecting laterally static and laterally dynamic objects, which the vehicle is to drive past, as lateral boundary objects. A respective lateral distance of the vehicle from the respective lateral boundary object is detected. A speed of the respective laterally dynamic object is determined and at least the respectively laterally dynamic object is classified according to its type. A set of characteristic curves is stored in a control unit of the vehicle, the characteristic curves of the set being assigned in each case to an environmental situation predetermined depending on lateral boundary objects. It is predetermined by a respective characteristic curve for the respective environmental situation at what maximum speed the vehicle is to drive past a lateral boundary object at different lateral distances from the latter.

A method of maintaining vehicle formation includes receiving a desired cross track offset distance and a desired along track offset distance between a lead vehicle and a follower vehicle; receiving a current position, a current yaw rate, and a current speed of the lead vehicle; determining a current turn radius of the lead vehicle based on the current yaw rate and the current speed of the lead vehicle; determining a projected turn radius of the follower vehicle based on the current turn radius of the lead vehicle, the desired cross track offset distance, and the desired along track offset distance; determining a commanded curvature and a next speed of the follower vehicle based on a current position of the follower vehicle and the projected turn radius of the follower vehicle; and outputting the next speed and the commanded curvature to a control system of the follower vehicle.

In a traffic emergency, there is no time for a human to integrate multiple sensor data streams and devise a plan for avoiding a collision. Only the electronic reflexes of a trained automatic system can provide evasive action in time. Disclosed is an artificial intelligence (AI) model trained to recognize an imminent collision based on sensor data, rapidly devise and test a large number of possible sequences of actions, some drawn from a library of previously-successful strategies and others invented by the AI model. If any sequence can avoid the collision, the AI model implements that sequence immediately. If none of the sequences can avoid the collision, the AI model calculates the harm caused by each sequence and picks the one that causes the least harm (fatalities, injuries, etc.) for implementation. AI is needed to find a possible solution in time to implement it and thereby mitigate the imminent collision.

A system and method for navigating a vehicle automatically from a current location to a destination location without a human operator is disclosed. The method includes identifying a vehicle location using global positioning system (GPS) data regarding the vehicle. Also included is identifying that the vehicle location is near or at a parking location. Then, using mapping data defined for the parking location. The mapping data at least in part is used to find a path at the parking location to avoid a collision of the vehicle with at least one physical structure when the vehicle is automatically moved at the parking location. The method includes instructing the electronics of the vehicle to proceed with controlling the vehicle to automatically move from the current location to the destination location at the parking location. The electronics use as input at least part of the mapping data and sensor data collected from around the vehicle by at least two vehicle sensors. The path is configured to be updatable dynamically based on changes in the destination location or changes along the path. The destination location is a parking spot for the vehicle at the parking location.

A cooperative adaptive cruise control (CACC) system acquires a driving pattern of a target vehicle and variably provides an inter-vehicle distance and a responsible speed level of a subject vehicle that are followed by the CACC system based on the driving pattern. The CACC system includes a communication unit receiving vehicle information and road information of a region in which the subject vehicle travels; an information collection unit collecting driving information of a forward vehicle, vehicle information of the subject vehicle, and the road information; and a control unit controlling the inter-vehicle distance and the responsible speed level of the CACC system based on the driving pattern of the target vehicle according to generated control information.

Disclosed are methods and apparatuses for signaling turn safety. One or more sensor readings can be received. The one or more sensor readings can be compared to one or more thresholds. A signal can be provided to one or more visual indicators based on whether the one or more sensor readings satisfy the one or more thresholds.

Techniques and methods for determining regions. For instance, a vehicle may determine a trajectory of the vehicle and a trajectory of an agent, such as a pedestrian. The vehicle may then determine one or more contextual factors. In some examples, the one or more contextual factors are associated with a location of the agent with respect to a crosswalk, a location of the vehicle with respect to the crosswalk, a state of the crosswalk, and/or the like. The vehicle may then determine the region using the trajectory of the vehicle, the trajectory of the agent, and the one or more contextual factors. Additionally, using a time buffer value and a distance buffer value associated with the region, the vehicle may determine whether to yield to the agent within the region.

Systems and methods are described herein for managing communications for a connected vehicle, such as between the connected vehicle and other connected vehicle and/or between the connected vehicle and infrastructure entities, such as providers of services to the connected vehicle. For example, a communication network, such as a network provided by a network carrier, may include various cloud engines or other network-based servers that manage, coordinate, and/or provision communications between the connected vehicle and other parties, such as vehicles, road devices, buildings, and other infrastructure entities.

Aspects of the disclosure relate to generating a speed plan for an autonomous vehicle. As an example, the vehicle is maneuvered in an autonomous driving mode along a route using pre-stored map information. This information identifies a plurality of keep clear regions where the vehicle should not stop but can drive through in the autonomous driving mode. Each keep clear region of the plurality of keep clear regions is associated with a priority value. A subset of the plurality of keep clear regions is identified based on the route. A speed plan for stopping the vehicle is generated based on the priority values associated with the keep clear regions of the subset. The speed plan identifies a location for stopping the vehicle. The speed plan is used to stop the vehicle in the location.