In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Path specific rolling resistance is determined in a method including: receiving, from a first source, first rolling resistance data of a first set of road surface portions of a corresponding first set of times; receiving, from a second source, second rolling resistance data of a second set of road surface portions of a corresponding second set of times; determining, using any of the first rolling resistance data and the second rolling resistance data, a path specific rolling resistance for a given path including one or more of the road surface portions selected from the first and second set of road surface portions; and providing at least one target vehicle with the path specific rolling resistance or a derivative of the path specific rolling resistance.

An advanced driver assistance system (ADAS) and method for a vehicle include a light intensity detection system configured to detect a set of light density changes proximate to a face of a driver of the vehicle, a vehicle-to-everything (V2X) communication system, a global navigation satellite system (GNSS) system, and a controller configured to detect a current glare condition based on the set of detected light density changes from the light intensity detection system, share, using the V2X communication system, the detected current glare condition and the corresponding GNSS location, based on the sharing and another set of parameters, determine whether the vehicle is likely to experience a future glare condition during a future period, and in response to determining that the vehicle is likely to experience the future glare condition during the future period, at least one remedial action.

A method of sensing a three-dimensional (3D) space using at least one sensor is proposed. The method can include acquiring spatial information over time for the sensed 3D space, applying a neural network based object classification model to the acquired spatial information over time to identify at least one object in the sensed 3D space. The method can also include tracking the sensed 3D space including the identified at least one object, and using information related to the tracked 3D space.

A system and corresponding method for autonomous driving of a vehicle are provided. The system comprises at least one neural network (NN) that generates at least one output for controlling the autonomous driving. The system further comprises a main data path that routes bulk sensor data to the at least one NN and a low-latency data path with reduced latency relative to the main data path. The low-latency data path routes limited sensor data to the at least one NN which, in turn, employs the limited sensor data to improve performance of the at least one NN's processing of the bulk sensor data for generating the at least one output. Improving performance of the at least one NN's processing of the bulk sensor data enables the system to, for example, identify a safety hazard sooner, enabling the autonomous driving to divert the vehicle and avoid contact with the safety hazard.

A vehicular control system includes a plurality of sensors disposed at a vehicle and sensing exterior of the vehicle. An electronic control unit (ECU) includes a processor that processes sensor data captured by the sensors. The ECU, responsive at least in part to processing of captured sensor data as the vehicle travels in a traffic lane of a multi-lane road, determines a rearward approaching vehicle rearward of the equipped vehicle that is in an adjacent traffic lane. The ECU determines a leading vehicle ahead of the equipped vehicle and traveling in the same traffic lane as the equipped vehicle. The ECU controls the equipped vehicle to accelerate the vehicle to at least match the speed of the determined rearward approaching vehicle and to maneuver into the adjacent traffic lane to pass the determined leading vehicle ahead of the determined rearward approaching vehicle.

A control device for a vehicle for determining perceptual load of a visual and dynamic driving scene, the control device being configured to: receive an image sequence representing the driving scene, extract a set of scene features from the image sequence, the set of scene features representing static and/or dynamic information of the driving scene, calculate a time-aggregated representation of the image sequence based on the extracted set of scene features, calculate an attention map of the driving scene by attentional pooling of the time-aggregated representation of the image sequence, and determine the perceptual load of the driving scene based on the attention map. The invention further relates to a corresponding method.

The present disclosure describes a computer-implemented method for controlling a vehicle. In aspects, the computer-implemented method includes acquiring sensor data from a sensor, determining first processed data related to a first area around the vehicle based on the sensor data using a machine-learning method, and determining second processed data related to a second area around the vehicle based on the sensor data using a conventional method. The second area may include a subarea of the first area. In addition, the computer-implemented method includes controlling the vehicle based on the first processed data and the second processed data.

A vehicular control system includes a plurality of sensors disposed at a vehicle and sensing exterior of the vehicle. An electronic control unit (ECU) includes a processor that processes sensor data captured by the sensors. The vehicular control system, responsive at least in part to processing at the ECU of captured sensor data as the vehicle travels in a traffic lane of a road, detects another vehicle that is rearward of the equipped vehicle and traveling along an adjacent traffic lane. The vehicular control system detects a leading vehicle ahead of the equipped vehicle and traveling in the same traffic lane as the equipped vehicle. The vehicular control system, responsive to determination of a space along the other traffic lane ahead of the detected other vehicle, controls the equipped vehicle to maneuver into the adjacent traffic lane to pass the detected leading vehicle ahead of the detected other vehicle.

A recognition processing apparatus includes a video acquisition unit configured to acquire first captured image data of surroundings of a host vehicle captured by a far-infrared camera, an other vehicle detection unit configured to detect another vehicle parked or stopped in the surroundings of the host vehicle, a heat detection unit configured to detect radiated heat associated with an operation of the other vehicle based on a thermal distribution in a region corresponding to the other vehicle in the first captured image data, and a person detection unit configured to, when the radiated heat has been detected, preferentially execute person recognition in a vicinity of the other vehicle to detect a person. The recognition processing apparatus can ascertain the possibility that a person gets out of a detected vehicle and promptly detect such a person.