The TOR Agent:Optimizing Driver Take-Over with Reinforcement Learning

Kuen, Jakob; Schartmüller, Clemens; Wintersberger, Philipp

doi:10.1145/3473682.3480262

Cited by 1 publication

(1 citation statement)

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“…Deo and Trivedi proposed a long short-term memory (LSTM) network model for the continuous prediction of driver readiness [84]. Kuen et al proposed a TOR agent that learns to issue TOR at the most appropriate time [85]. To be even more effective in determining the timing and type of TOR, Yang et al introduced a 3D convolutional neural network to recognize drivers' current activity [86].…”

Section: Related Work On Modeling In the Field Of Driving Automationmentioning

confidence: 99%

Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving

Gruden

Jakus

2023

Applied Sciences

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In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.

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Section: Related Work On Modeling In the Field Of Driving Automationmentioning

confidence: 99%