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Adaptive Evolutionary Framework for Safe, Efficient, and Cooperative Autonomous Vehicle Interactions

Yijun Lu, Zhen Tian, Zhihao Lin

TL;DR

Simulation results demonstrate the proposed EEGT outperforms EGT and popular benchmark games in terms of lower collision rates, improved safety distances, higher speeds, and overall better performance compared to Nash and Stackelberg games across diverse scenarios and parameter settings.

Abstract

Modern transportation systems face significant challenges in ensuring road safety, given serious injuries caused by road accidents. The rapid growth of autonomous vehicles (AVs) has prompted new traffic designs that aim to optimize interactions among AVs. However, effective interactions between AVs remains challenging due to the absence of centralized control. Besides, there is a need for balancing multiple factors, including passenger demands and overall traffic efficiency. Traditional rule-based, optimization-based, and game-theoretic approaches each have limitations in addressing these challenges. Rule-based methods struggle with adaptability and generalization in complex scenarios, while optimization-based methods often require high computational resources. Game-theoretic approaches, such as Stackelberg and Nash games, suffer from limited adaptability and potential inefficiencies in cooperative settings. This paper proposes an Evolutionary Game Theory (EGT)-based framework for AV interactions that overcomes these limitations by utilizing a decentralized and adaptive strategy evolution mechanism. A causal evaluation module (CEGT) is introduced to optimize the evolutionary rate, balancing mutation and evolution by learning from historical interactions. Simulation results demonstrate the proposed CEGT outperforms EGT and popular benchmark games in terms of lower collision rates, improved safety distances, higher speeds, and overall better performance compared to Nash and Stackelberg games across diverse scenarios and parameter settings.

Adaptive Evolutionary Framework for Safe, Efficient, and Cooperative Autonomous Vehicle Interactions

TL;DR

Simulation results demonstrate the proposed EEGT outperforms EGT and popular benchmark games in terms of lower collision rates, improved safety distances, higher speeds, and overall better performance compared to Nash and Stackelberg games across diverse scenarios and parameter settings.

Abstract

Modern transportation systems face significant challenges in ensuring road safety, given serious injuries caused by road accidents. The rapid growth of autonomous vehicles (AVs) has prompted new traffic designs that aim to optimize interactions among AVs. However, effective interactions between AVs remains challenging due to the absence of centralized control. Besides, there is a need for balancing multiple factors, including passenger demands and overall traffic efficiency. Traditional rule-based, optimization-based, and game-theoretic approaches each have limitations in addressing these challenges. Rule-based methods struggle with adaptability and generalization in complex scenarios, while optimization-based methods often require high computational resources. Game-theoretic approaches, such as Stackelberg and Nash games, suffer from limited adaptability and potential inefficiencies in cooperative settings. This paper proposes an Evolutionary Game Theory (EGT)-based framework for AV interactions that overcomes these limitations by utilizing a decentralized and adaptive strategy evolution mechanism. A causal evaluation module (CEGT) is introduced to optimize the evolutionary rate, balancing mutation and evolution by learning from historical interactions. Simulation results demonstrate the proposed CEGT outperforms EGT and popular benchmark games in terms of lower collision rates, improved safety distances, higher speeds, and overall better performance compared to Nash and Stackelberg games across diverse scenarios and parameter settings.

Paper Structure

This paper contains 14 sections, 33 equations, 21 figures, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. System Structure
  3. Methodology
  4. Vehicle Kinematic
  5. Causal Inference Mechanism
  6. Evolutionary Game Theory
  7. Imitation and Mutation in Evolutionary Game Theory
  8. General Algorithm for Multi-Vehicle Interaction
  9. Lane-Changing intention using Quintic Polynomial Path Planning
  10. Collision Detection During Lane Change
  11. Simulation Results
  12. Simulation Case 1
  13. Simulation Case 2
  14. Conclusion

Figures (21)

  • Figure 1: Unsafe interactions between AVs due to their varied demands.
  • Figure 2: The causal evaluation-based evolutionary game theory for safe, efficienct, and cooperative interactive driving.
  • Figure 3: An example of causal influence.
  • Figure 4: Causal evaluation-based evolutionary game.
  • Figure 5: Illustration of quintic polynomial path planning. (a) Lane-changing process with another vehicle on the road. (b) Variation of longitudinal speed, lateral speed, longitudinal acceleration, and steering angle.
  • ...and 16 more figures