US 12,391,281 B2
Responses to vulnerable road user's adversarial behavior
Alaa M. Khamis, Courtice (CA); Wei Tong, Troy, MI (US); and Robert Dale Burns, Lake Orion, MI (US)
Assigned to GM GLOBAL TECHNOLOGY OPERATIONS LLC, Detroit, MI (US)
Filed by GM Global Technology Operations LLC, Detroit, MI (US)
Filed on May 17, 2023, as Appl. No. 18/318,826.
Prior Publication US 2024/0383501 A1, Nov. 21, 2024
Int. Cl. B60W 60/00 (2020.01); B60G 5/00 (2006.01); B60Q 1/50 (2006.01); B60Q 5/00 (2006.01); G05D 1/00 (2024.01)
CPC B60W 60/0015 (2020.02) [B60G 5/005 (2013.01); B60Q 1/507 (2022.05); B60W 60/0053 (2020.02); G05D 1/00 (2013.01); B60W 2554/4029 (2020.02); B60W 2554/4046 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A method of responding to adversarial behavior directed toward an autonomous vehicle, comprising:
determining, with a pedestrian detection system, a vehicle controller and an adversarial intent algorithm, that a risk level of adversarial behavior from at least one pedestrian within proximity of the autonomous vehicle is one of no risk of adversarial behavior, low risk of adversarial behavior or high risk of adversarial behavior by:
analyzing, with a crossing behavior algorithm, crossing behaviors of the at least one pedestrian and determining if crossing behaviors of the at least one pedestrian are adversarial based on whether the autonomous vehicle has the right of way, the type of crossing including any signs, lights or signals present, and how long the at least one pedestrian takes to complete the crossing;
analyzing, with a non-verbal cue algorithm, non-verbal cues displayed by the at least one pedestrian and determining if non-verbal cues displayed by the at least one pedestrian are adversarial based on comparing captured images and body gestures and actions of the at least one pedestrian to known patterns of adversarial behavior;
analyzing, with an audible cues algorithm, audible cues displayed by the at least one pedestrian and determining if audible cues displayed by the at least one pedestrian are adversarial based on comparing captured acoustic signals from the at least one pedestrian to known sounds that indicate adversarial behavior; and
combining, with an information fusion algorithm, information from the crossing behavior algorithm, the non-verbal cue algorithm and the audible cues algorithm; and
providing, with the vehicle controller, actions to be performed by the autonomous vehicle in response to the adversarial behavior based on the risk level of the adversarial behavior.