Steering Wheel Interaction Design Based on Level 3 Autonomous Driving Scenario

Abstract Inverse reinforcement learning (IRL) has been successfully applied in many robotics and autonomous driving studies without the need for hand-tuning a reward function. However, it suffers from safety issues. Compared to the reinforcement learning (RL) algorithms, IRL is even more vulnerable to unsafe situations as it can only infer the importance of safety based on expert demonstrations. In this paper, we propose a safety-aware adversarial inverse reinforcement learning algorithm (S-AIRL). First, the control barrier function (CBF) is used to guide the training of a safety critic, which leverages the knowledge of system dynamics in the sampling process without training an additional guiding policy. The trained safety critic is then integrated into the discriminator to help discern the generated data and expert demonstrations from the standpoint of safety. Finally, to further improve the safety awareness, a regulator is introduced in the loss function of the discriminator training to prevent the recovered reward function from assigning high rewards to the risky behaviors. We tested our S-AIRL in the highway autonomous driving scenario. Comparing to the original AIRL algorithm, with the same level of imitation learning (IL) performance, the proposed S-AIRL can reduce the collision rate by 32.6%.

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Automatisiertes Fahren: Was fehlt noch fur einen sicheren Rechtsrahmen fur Level 3? /Legislation Needed: What is Missing to Make Level 3 Autonomous Driving Legal?

41. Internationales Wiener Motorensymposium 22.-24. April 2020 ◽

10.51202/9783186813121-i-411 ◽

2020 ◽

pp. I-411-I-418

Author(s):

M. Siemann

Keyword(s):

Autonomous Driving ◽

Level 3 ◽

Automatisiertes Fahren

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Multimodal Cue Combinations: A Possible Approach to Designing In-Vehicle Takeover Requests for Semi-autonomous Driving

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181319631053 ◽

2019 ◽

Vol 63 (1) ◽

pp. 1739-1743

Author(s):

Gaojian Huang ◽

Clayton Steele ◽

Xinrui Zhang ◽

Brandon J. Pitts

Keyword(s):

Autonomous Vehicles ◽

Response Times ◽

Autonomous Driving ◽

Multimodal Signals ◽

Road Sign ◽

Sign Detection ◽

Level 3 ◽

Perceived Workload ◽

Tactile Cues ◽

Vehicle Automation

The rapid growth of autonomous vehicles is expected to improve roadway safety. However, certain levels of vehicle automation will still require drivers to ‘takeover’ during abnormal situations, which may lead to breakdowns in driver-vehicle interactions. To date, there is no agreement on how to best support drivers in accomplishing a takeover task. Therefore, the goal of this study was to investigate the effectiveness of multimodal alerts as a feasible approach. In particular, we examined the effects of uni-, bi-, and trimodal combinations of visual, auditory, and tactile cues on response times to takeover alerts. Sixteen participants were asked to detect 7 multimodal signals (i.e., visual, auditory, tactile, visual-auditory, visual-tactile, auditory-tactile, and visual-auditory-tactile) while driving under two conditions: with SAE Level 3 automation only or with SAE Level 3 automation in addition to performing a road sign detection task. Performance on the signal and road sign detection tasks, pupil size, and perceived workload were measured. Findings indicate that trimodal combinations result in the shortest response time. Also, response times were longer and perceived workload was higher when participants were engaged in a secondary task. Findings may contribute to the development of theory regarding the design of takeover request alert systems within (semi) autonomous vehicles.

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