Volvo Car Corporation

Zenseact Ab

Zhennan Fei

Majid Khorsand Vakilzadeh

Mattias Brännström

Junsheng Fu

Mats Bohman

Anna Stina Ida Carlsson

Carl Anders Eric Ödblom

Pär Bertil Gottfrid Nilsson

Gabriel Rodrigues De Campos

Jonas Krook

Xuan Zhang

A method for managing an Operational Design Domain (ODD) expansion for an Automated Driving System (ADS) of a vehicle is disclosed. The method includes: obtaining a plurality of scenario parameters defining the environment of the vehicle, wherein each scenario parameter corresponds to a respective condition of the ODD; in response to the plurality of scenario parameters fulfilling the plurality of ODD conditions: allowing the ADS to be activated or to remain active; if not: querying a logic formula based on the obtained plurality of scenario parameters, such that if at least one soft constraint of the logical formula is satisfied without violating any hard constraints of the logical formula, and allowing the ADS to be activated in a degraded mode by limiting one or more variable state parameters of the ADS to conform to the determined set of values of the one or more variable state parameters.

Computer-implemented method for managing an operational design domain's expansion for an automated driving system

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.

Artificially intelligent provision of post-vehicular-collision evidence

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.

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.

Precautionary vehicle path planning

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.

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Gothenburg, Sweden

Zhennan Fei