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Comparative Study of Cooperative Platoon Merging Control Based on Reinforcement Learning
1Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309, USA.
Sensors (Basel, Switzerland)
|January 21, 2023
Summary
Optimizing cooperative driving for autonomous electric vehicles during lane reductions significantly cuts energy use and improves comfort. This research demonstrates substantial reductions in energy consumption and jerk through intelligent merge strategies.
Area of Science:
- Intelligent Transportation Systems
- Autonomous Vehicle Control
- Deep Reinforcement Learning
Background:
- Vehicle merging in lane reductions impacts safety, comfort, and energy consumption, crucial for autonomous electric vehicle (AEV) adoption.
- Cooperative driving strategies are needed to optimize traffic flow and energy efficiency in AEV platoons.
Purpose of the Study:
- To analyze optimal cooperative driving behaviors for connected and automated vehicles during merging.
- To reduce energy consumption and enhance traffic flow in a two-platoon merging scenario.
Main Methods:
- Utilized a model-free deep reinforcement learning approach to determine optimal driving strategies.
- Simulated a scenario where two platoons merge into a single lane.
- Analyzed key metrics including merge time, energy consumption, and jerk.
Main Results:
- Achieved up to a 76.7% reduction in energy consumption.
- Decreased average jerk by up to 50% through cooperative merge behavior.
- Demonstrated significant improvements in passenger comfort and drivability.
Conclusions:
- Cooperative merge behavior in AEVs is critical for enhancing efficiency and passenger experience.
- Reducing jerk minimizes acceleration oscillations, improving comfort and acceptance of autonomous platooning.
- The findings support the broader adoption of autonomous electric vehicle technology.
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