Dynamic Driving Task Fallback for an Automated Driving System whose Ability to Monitor the Driving Environment has been Compromised

Abstract : An Automated Driving System (ADS) is subject to hazardous weather conditions and to failures, both of which can result in a partial or total loss of its ability to monitor the driving environment. Yet until high driving automation and full driving automation is achieved, a human driver is expected to respond appropriately to any malfunction or adverse on-road conditions preventing the ADS from reliably sustaining the dynamic driving task performance. However, automation causes drowsiness and hypo-vigilance, which can compromise a human driver's ability to respond to ADS-issued requests. Hence the necessity of defining dynamic driving task fallback strategies that can be performed by the ADS, if and when necessary. The proposed fallback strategy is aimed at level 4 ADS features designed to operate a vehicle on a road whose characteristics make any attempt at stopping hazardous. It naturally applies to level 5 ADS-operated vehicles and to ADS-dedicated vehicles as well. The transition stage, during which the strategy is triggered, consists in the replacement of missing vehicles and obstacles in the world model with ghost objects. An embedded visibility map is then used to retrieve the maximum distance at which the ADS-operated vehicle can be seen, when driving behind it. The speed profile underlying the fallback strategy meets a time to collision criterion of 4 s, which enables the avoidance and the mitigation of rear-end collisions. The behaviour of drivers in collision imminent situations cannot be observed in test track studies due to safety concerns. As a result, experiments were conducted in the driving simulation software SCANeR studio.
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Submitted on : Tuesday, March 6, 2018 - 9:06:02 PM
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Yrvann Emzivat, Javier Ibanez-Guzman, Philippe Martinet, Olivier Henri Roux. Dynamic Driving Task Fallback for an Automated Driving System whose Ability to Monitor the Driving Environment has been Compromised. IEEE Intelligent Vehicles Symposium, Jun 2017, Redondo Beach, United States. ⟨hal-01724931⟩



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