Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can receive one or more electronic notifications broadcasted by a vehicle. In various aspects, the system can determine, via parsing, whether the one or more electronic notifications indicate that a vehicular collision not involving the vehicle has occurred in a vicinity of the vehicle. In various instances, the system can initiate, in response to a determination that the one or more electronic notifications indicate that the vehicular collision has occurred, one or more electronic actions based on the vehicular collision.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a first vehicle. In various aspects, the system can capture, via one or more first cameras or one or more first microphones of the first vehicle, vicinity data associated with a first vicinity of the first vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the first vehicle has occurred in the first vicinity of the first vehicle. In various cases, the system can record, in response to a determination that the vehicular collision has occurred and via the one or more first cameras or the one or more first microphones, first post-collision evidence associated with the first vicinity of the first vehicle.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle and can capture, via one or more cameras or microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can generate, via execution of a deep learning neural network on the vicinity data, a classification label indicating whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can record, in response to the classification label indicating that the vehicular collision has occurred and via the one or more cameras or the one or more microphones, post-collision evidence associated with the vicinity of the vehicle. In various aspects, the system can broadcast the classification label and the post-collision evidence to an emergency service computing device.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle. In various aspects, the system can capture, via one or more cameras or one or more microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can broadcast, in response to a determination that the vehicular collision has occurred, via the one or more cameras or the one or more microphones, and to an emergency service computing device, a post-collision live stream associated with the vicinity of the vehicle.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle. In various aspects, the system can capture, via one or more cameras or one or more microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can broadcast, in response to a determination that the vehicular collision has occurred and via the one or more cameras or the one or more microphones, one or more electronic notifications.

Abstract: An MRM planning system for supporting planning of a minimal risk maneuver, MRM, of an Automated Driving System. The system determines a remaining distance to an upcoming operational design domain, ODD, exit at which an ODD defined for the autonomous driving mode is planned to end. The system further assesses, when the remaining distance is shorter than a predeterminable distance, data associated with a stretch of road leading up to the ODD exit. The system identifies based on the assessment of the data, a favourable region along the stretch of road for potentially bringing the vehicle to a stop deemed safe subsequent a potential MRM being triggered. The system determines a timing at which to initiate an autonomous driving mode DDT termination procedure at which to provide a handover request prompting an occupant of the vehicle to take over the DDT.

Abstract: The present disclosure relates to a method, system, a vehicle and a computer-readable storage medium. The method comprises determining a fulfilment of a set of predefined Operational Design Domain (ODD) conditions for an Automated Driving System (ADS) feature of an ego-vehicle at least partly based on sensor data obtained from on-board sensors of the ego-vehicle and determining a first ODD exit boundary along an estimated route to be travelled by the ego-vehicle. The method further comprises obtaining data from at least one external vehicle located on the estimated route to be travelled by the ego-vehicle. Further, the method comprises evaluating the obtained data from the at least one external vehicle against an ODD assessment scheme to determine a fulfilment of the predefined ODD conditions in the surrounding environment of the external vehicle and determining a second ODD exit boundary along the estimated route of the ego-vehicle based on the evaluation.

Abstract: A path-adapting system for precautionary path planning of a host vehicle. The path-adapting system determines conditions of a liquid and/or solid loose material in the air and/or on the road surface in vicinity of the host vehicle, determines movement attributes in relation to the host vehicle of at least a first object in host vehicle surroundings, determines based on the conditions and movement attributes, at least a first estimated upcoming host vehicle position occurring at an estimated upcoming at least first time instance at which the at least first detected object is estimated to direct the material onto the host vehicle and/or the host vehicle is estimated to direct the material onto the at least first detected object, and determines a driving path, altering for the at least first time instance the at least first estimated upcoming host vehicle position to a modified host vehicle position.

Abstract: A method performed by a scenario reconstruction system for supporting assessment of unforeseen driving scenarios of a host vehicle. The scenario reconstruction system stores timestamped sensor data comprising the host vehicle's geographical position and surroundings captured with support from one or more surrounding detecting sensors onboard the host vehicle. The system detects at a scenario time instance, involvement of the host vehicle in an unforeseen driving scenario. Moreover, the scenario reconstruction system derives from the timestamped sensor data, subsets of sensor data and identifies one or more surrounding detecting sensors-provided entities respectively present in the subsets pertinent at least two respective separate timestamps.