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Towards human-like speed control in autonomous vehicles: A mountainous freeway case
Zhigui Chen1, Xuesong Wang1, Qiming Guo2
1School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
Accident; Analysis and Prevention
|January 13, 2022
Summary
Developing human-like speed control for autonomous vehicles (AVs) is crucial for user acceptance. Current strategies like cruise control (CC) and road-limiting control (RC) fail on complex roads, unlike the proposed human-focused strategy.
Area of Science:
- Autonomous Vehicle Technology
- Human-Computer Interaction
- Road Safety Engineering
Background:
- Existing autonomous vehicle (AV) motion strategies primarily focus on interactions with other vehicles, neglecting occupant comfort and preferences.
- Current baseline speed strategies, such as constant speed cruise control (CC) and road-limiting control (RC), do not account for human-like speed preferences, especially in free-flow conditions on complex roadways.
- There is a research gap in evaluating AV speed control strategies for occupant comfort and acceptance on challenging road geometries during free-flow driving.
Purpose of the Study:
- To evaluate the limitations of current cruise control (CC) and road-limiting control (RC) strategies for autonomous vehicles (AVs) in free-flow conditions on mountainous freeways with complex alignments.
- To investigate human speed selection behavior in simulated driving scenarios on complex road alignments.
- To develop and propose a human-like speed control (HC) strategy that incorporates occupant preferences for baseline speed profiles.
Main Methods:
- Utilized the Tongji University driving simulator to assess CC and RC strategies under free-flow conditions on a mountainous freeway with complex alignments.
- Observed and analyzed human speed selection behavior of participating drivers.
- Applied clustering analysis to identify distinct driving styles (slow, fast, consistent) and developed analytical models for human-focused, road-dependent baseline speed profiles.
Main Results:
- The study revealed significant limitations of existing CC and RC strategies when applied to roads with complex alignments, failing to meet occupant expectations.
- Clustering analysis identified three distinct human driving styles, enabling the creation of human-focused baseline speed profiles.
- The proposed human-like speed control (HC) strategy demonstrated considerable discrepancies compared to CC and RC, highlighting the inadequacy of existing methods for human-centric driving.
Conclusions:
- Existing autonomous vehicle speed control strategies (CC and RC) are insufficient for ensuring occupant comfort and acceptance on complex road alignments.
- A human-like speed control (HC) strategy, derived from observed human driving behaviors and preferences, is necessary for enhancing the user experience in autonomous vehicles.
- Future research should focus on implementing and validating human-centric speed control strategies in real-world autonomous driving scenarios.
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