A path providing device configured to provide a path information to a vehicle includes: a communication unit configured to receive map information from a server, the map information including a plurality of layers of data, an interface unit configured to receive sensing information from one or more sensors disposed at the vehicle, the sensing information including an image received from an image sensor, and a processor configured determine an optimal path for guiding the vehicle from an identified lane, generate autonomous driving visibility information and transmit the generated autonomous driving visibility information based on the sensing information and the determined optimal path, update the optimal path based on dynamic information related to a movable object located in the optimal path and the autonomous driving visibility information, and control the interface unit to shut off or start an engine of the vehicle based on the autonomous driving visibility information.

A method for controlling emergency stop of an autonomous vehicle is provided. The method includes: controlling emergency stop of an autonomous vehicle capable of recognizing a stop request from an emergency vehicle, determining whether a current situation is an emergency situation or a general situation, and performing a procedure corresponding to each situation to stop the autonomous vehicle based on each situation.

The present disclosure relates to a method for determining a state of a vehicle on a road portion having two or more lanes. The method comprises obtaining map data associated with the road portion and positioning data indicating a position of the vehicle on the road and a sensor data from a sensor system of the vehicle. The method further comprises initializing a filter per lane of the road portion based on the obtained map data, the obtained positioning data, and the obtained sensor data, wherein each filter indicates an estimated state of the vehicle on the road portion. Then, selecting one of the initialized filters using a trained machine-learning algorithm, configured to use the obtained map data, the positioning data, the sensor data, and each estimated state as indicated by each filter as input and to output a current state of the vehicle on the road portion.

An electronic device provided in an autonomous vehicle, the electronic device comprising a camera, a memory storing at least one instruction, and at least one processor operatively coupled with the camera, wherein the at least one processor is configured to, when the at least one instruction is executed obtain a front image in which the autonomous vehicle is driving through the camera, identify a target vehicle in the front image based on the vehicle detection model stored in the memory, generate a bounding box corresponding to the target vehicle in response to an identification of the target vehicle, generate a sliding window having a height equal to the height of the bounding box and having a width half of the width of the bounding box, divide the bounding box into a first area positioned left based on a middle position of the width of the sliding window and a second area positioned right based on the middle position, generate an extended bounding box by extending the first area in a left direction and extending the second area in a right direction, wherein size of the extended bounding box is twice as wide as size of the bounding box, obtain a sum of a pixel difference values between the first area and the second area for each shift by sequentially shifting the sliding window by a predefined pixel interval with respect to all of width of the extended bounding box, and identify a point that corresponds to a minimum value among sum values respectively indicating the sums that are obtained according to the shifting.

A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.

A traffic light recognition-based smart cruise control (SCC) control apparatus and method. The apparatus includes an image-capturing unit configured to capture a front-side image of a vehicle, and a controller configured to classify at least one traffic light in the front-side image, calculate a distance to each classified traffic light, if there are a plurality of traffic lights having different distances in the front-side image, recognize a first signal of a first traffic light closest to the vehicle and a second signal of a second traffic light located next to the first traffic light, if the first signal is a driving signal and the second signal is a stop signal, calculate an acceleration time, and control the vehicle to be accelerated and traveled during the acceleration time, and then stopped before the second traffic light by using frictional force.

A reversing control method and system and a vehicle, which relate to the technical field of vehicles. In the method, after receiving a reversing instruction for a vehicle, a first reversing trajectory of the vehicle can be acquired; the vehicle can be controlled to reverse according to the first reversing trajectory, such that a driver does not need to control the vehicle, thus avoiding tedious operation by the driver; during a reversing process the first reversing trajectory can be adjusted according to first environmental obstacle information of the vehicle, to acquire a second reversing trajectory; and the vehicle is then controlled to reverse according to the second reversing trajectory. Thus, the danger caused by environmental obstacles during a reversing process may be avoided, and potential safety hazards in process of reversing a vehicle are eliminated.

A method and system for a vehicle control system generates maps utilized for charting a path of a vehicle through an environment. The method performed by the system obtains information indicative of vehicle movement from at least one vehicle system and images including objects within an environment from a camera mounted on the vehicle. The system uses the gathered information and images to create a depth map of the environment. The system also generates an image point cloud map from images taken with a vehicle camera and a radar point cloud map with velocity information from a radar sensor mounted on the vehicle. The depth map and the point cloud maps are fused together and any dynamic objects filtered out from the final map used for operation of the vehicle.

System, methods, and computer-readable media for training an object path prediction model to reduce an uncertainty of a predicted path when the predicted path of an object adjacent to another object. The training penalizes an uncertainty area prediction associated with a predicted future location of a nearby object to an autonomous vehicle (AV) when the uncertainty area prediction overlaps with another object to which the first detected object would be adjacent at the predicted future location. The training also penalizes a set of predicted future locations that implies improbable vehicle kinematics, whereby the object path prediction model becomes trained to avoid predicting similar sets of predicted future locations with improbable vehicle kinematics.

Methods, systems, and computer readable mediums for determining a scene representation for a vehicle are provided. A disclosed method includes acquiring data from one or more sensors of the vehicle. The method further includes detecting, using processing circuitry, one or more objects in a surrounding of the vehicle based on the acquired data; determining whether an object of the detected one or more objects intersects with a vehicle lane of travel; determining whether the object is in a lane of travel of the vehicle; determining inter-vehicle gaps and cut-ins between the object and the vehicle; and rendering a scene representation including the inter-vehicle gaps and cut-ins between the object and the vehicle.