A system for identifying redundant road lane detections in map data and subsequently updating the map data to remove the redundant road lane detections is provided. The system may be configured to determine, based on sensor data, a plurality of road lane detections associated with a road link represented by the map data. The system is further configured to determine a cluster for the road link based on a clustering criterion. The system is further configured to establish a plurality of road lane detection groups based on connectivity of the road lane detections in the cluster. The plurality of road lane detection groups is evaluated to identify one or more redundant road lane detections based on one or more of a parallel-detection criterion or a heading difference criterion. The identified redundant road lane detections are used to update the map data by a computer-implemented update process.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, relates to the field of artificial intelligence and specifically to computer vision and deep learning technologies, and can be applied to video analysis scenarios. A specific implementation comprises: acquiring at least two frames of consecutive traffic images; identifying respectively a position of a target vehicle from the at least two frames of consecutive traffic images to obtain a position information set; determining a direction of travel and speed of the target vehicle according to the position information set; and comparing the direction of travel and speed of the target vehicle with a pre-generated vehicle vector field to determine whether the target vehicle is abnormal.

The present disclosure provides a method and apparatus for detecting a traffic anomaly, relates to the field of artificial intelligence and specifically to computer vision and deep learning technologies, and can be applied to video analysis scenarios. A specific implementation comprises: acquiring at least two frames of consecutive traffic images; identifying respectively a position of a target vehicle from the at least two frames of consecutive traffic images to obtain a position information set; determining a direction of travel and speed of the target vehicle according to the position information set; and comparing the direction of travel and speed of the target vehicle with a pre-generated vehicle vector field to determine whether the target vehicle is abnormal.

A traffic signal control system includes a processor configured to adjust a green time of a traffic signal based on vehicle class information on each of vehicles of a vehicle group approaching an intersection. The vehicle class information indicates whether the vehicle is an environmentally friendly vehicle or an environmentally unfriendly vehicle that is not the environmentally friendly vehicle.

An example operation includes one or more of initially determining, via one or more sensors on a transport, that the transport is approaching a one-way road in a wrong direction based on a slowing down of the transport and a movement of the transport toward the one-way road, notifying, via the one or more sensors, one or more occupants of the transport about the approaching, and in response to the transport continuing to approach in the wrong direction, slowing the transport, by a computer associated with the transport, to not permit entry into the one-way road.

An example operation includes one or more of initially determining, via one or more sensors on a transport, that the transport is approaching a one-way road in a wrong direction based on a slowing down of the transport and a movement of the transport toward the one-way road, notifying, via the one or more sensors, one or more occupants of the transport about the approaching, and in response to the transport continuing to approach in the wrong direction, slowing the transport, by a computer associated with the transport, to not permit entry into the one-way road.

A system receives confirmation that a vehicle has accepted automatic control imposition for a drive within a geo-fenced boundary. The system tracks travel of a plurality of vehicles, including the vehicle, within the geo-fenced boundary. The system may determine that the vehicle has a threshold likelihood of encountering at least one of another vehicle or a boundary of the geo-fence at a threshold speed or above and responsive to the determination, impose automatic control on the vehicle, including at least one of controlled braking or speed limiting.

A system receives confirmation that a vehicle has accepted automatic control imposition for a drive within a geo-fenced boundary. The system tracks travel of a plurality of vehicles, including the vehicle, within the geo-fenced boundary. The system may determine that the vehicle has a threshold likelihood of encountering at least one of another vehicle or a boundary of the geo-fence at a threshold speed or above and responsive to the determination, impose automatic control on the vehicle, including at least one of controlled braking or speed limiting.