An Adaptive Human Driver Model for Realistic Race Car Simulations
- URL: http://arxiv.org/abs/2203.01909v1
- Date: Thu, 3 Mar 2022 18:39:50 GMT
- Title: An Adaptive Human Driver Model for Realistic Race Car Simulations
- Authors: Stefan L\"ockel, Siwei Ju, Maximilian Schaller, Peter van Vliet, Jan
Peters
- Abstract summary: We provide a better understanding of race driver behavior and introduce an adaptive human race driver model based on imitation learning.
We show that our framework can create realistic driving line distributions on unseen race tracks with almost human-like performance.
- Score: 25.67586167621258
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Engineering a high-performance race car requires a direct consideration of
the human driver using real-world tests or Human-Driver-in-the-Loop
simulations. Apart from that, offline simulations with human-like race driver
models could make this vehicle development process more effective and efficient
but are hard to obtain due to various challenges. With this work, we intend to
provide a better understanding of race driver behavior and introduce an
adaptive human race driver model based on imitation learning. Using existing
findings and an interview with a professional race engineer, we identify
fundamental adaptation mechanisms and how drivers learn to optimize lap time on
a new track. Subsequently, we use these insights to develop generalization and
adaptation techniques for a recently presented probabilistic driver modeling
approach and evaluate it using data from professional race drivers and a
state-of-the-art race car simulator. We show that our framework can create
realistic driving line distributions on unseen race tracks with almost
human-like performance. Moreover, our driver model optimizes its driving lap by
lap, correcting driving errors from previous laps while achieving faster lap
times. This work contributes to a better understanding and modeling of the
human driver, aiming to expedite simulation methods in the modern vehicle
development process and potentially supporting automated driving and racing
technologies.
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