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Hypergame-based Cognition Modeling and Intention Interpretation for Human-Driven Vehicles in Connected Mixed Traffic

Jianguo Chen, Zhengqin Liu, Jinlong Lei, Peng Yi, Yiguang Hong, Hong Chen

TL;DR

This paper develops an inverse learning algorithm for distributed intention interpretation via vehicle-to-everything (V2X) communication, which extends the framework to both offline and online scenarios and introduces a distributed trajectory prediction and planning approach for CAVs, leveraging the learned parameters in real time.

Abstract

With the practical implementation of connected and autonomous vehicles (CAVs), the traffic system is expected to remain a mix of CAVs and human-driven vehicles (HVs) for the foreseeable future. To enhance safety and traffic efficiency, the trajectory planning strategies of CAVs must account for the influence of HVs, necessitating accurate HV trajectory prediction. Current research often assumes that human drivers have perfect knowledge of all vehicles' objectives, an unrealistic premise. This paper bridges the gap by leveraging hypergame theory to account for cognitive and perception limitations in HVs. We model human bounded rationality without assuming them to be merely passive followers and propose a hierarchical cognition modeling framework that captures cognitive relationships among vehicles. We further analyze the cognitive stability of the system, proving that the strategy profile where all vehicles adopt cognitively equilibrium strategies constitutes a hyper Nash equilibrium when CAVs accurately learn HV parameters. To achieve this, we develop an inverse learning algorithm for distributed intention interpretation via vehicle-to-everything (V2X) communication, which extends the framework to both offline and online scenarios. Additionally, we introduce a distributed trajectory prediction and planning approach for CAVs, leveraging the learned parameters in real time. Simulations in highway lane-changing scenarios demonstrate the proposed method's accuracy in parameter learning, robustness to noisy trajectory observations, and safety in HV trajectory prediction. The results validate the effectiveness of our method in both offline and online implementations.

Hypergame-based Cognition Modeling and Intention Interpretation for Human-Driven Vehicles in Connected Mixed Traffic

TL;DR

This paper develops an inverse learning algorithm for distributed intention interpretation via vehicle-to-everything (V2X) communication, which extends the framework to both offline and online scenarios and introduces a distributed trajectory prediction and planning approach for CAVs, leveraging the learned parameters in real time.

Abstract

With the practical implementation of connected and autonomous vehicles (CAVs), the traffic system is expected to remain a mix of CAVs and human-driven vehicles (HVs) for the foreseeable future. To enhance safety and traffic efficiency, the trajectory planning strategies of CAVs must account for the influence of HVs, necessitating accurate HV trajectory prediction. Current research often assumes that human drivers have perfect knowledge of all vehicles' objectives, an unrealistic premise. This paper bridges the gap by leveraging hypergame theory to account for cognitive and perception limitations in HVs. We model human bounded rationality without assuming them to be merely passive followers and propose a hierarchical cognition modeling framework that captures cognitive relationships among vehicles. We further analyze the cognitive stability of the system, proving that the strategy profile where all vehicles adopt cognitively equilibrium strategies constitutes a hyper Nash equilibrium when CAVs accurately learn HV parameters. To achieve this, we develop an inverse learning algorithm for distributed intention interpretation via vehicle-to-everything (V2X) communication, which extends the framework to both offline and online scenarios. Additionally, we introduce a distributed trajectory prediction and planning approach for CAVs, leveraging the learned parameters in real time. Simulations in highway lane-changing scenarios demonstrate the proposed method's accuracy in parameter learning, robustness to noisy trajectory observations, and safety in HV trajectory prediction. The results validate the effectiveness of our method in both offline and online implementations.
Paper Structure (25 sections, 1 theorem, 47 equations, 13 figures, 3 tables, 5 algorithms)

This paper contains 25 sections, 1 theorem, 47 equations, 13 figures, 3 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Trajectory Planning Games
  3. Objective Function
  4. Constraints
  5. Game Model
  6. Modeling Cognitive Structures among Vehicles under Hypergames
  7. Human Model
  8. Cognitive Limitation
  9. Perceptual Limitation
  10. Hypergames
  11. Level 0 and Level 1 Hypergames
  12. Level 2 Hypergame
  13. Cognitive Stability Analysis
  14. Inverse Learning-Based Intention Interpretation and Distributed Trajectory Planning
  15. Offline Scenario
  16. ...and 10 more sections

Key Result

Theorem 1

Under the cognitive threshold $\epsilon_c$, if the CAVs can observe the true parameters of the HVs $\theta_{0,\text{true}}$, then the subjectively rationalized strategy profile $\{s_i^*\}_{i \in \mathcal{C}} \cup \{s_0^*\}$ of the CAVs and HV forms an HNE under the perceptual threshold $L\epsilon_c$

Figures (13)

  • Figure 1: An example of the traffic scenario involving the interaction of one HV and four CAVs on a three-lane road. The trajectories of the vehicles are color-coded to represent their respective paths, while an RSU supports the coordinated maneuvers of CAVs.
  • Figure 2: Representation of Driving Styles, True and Average Weights, and the Cognitive Threshold ($\epsilon_c$). This illustrates the relationship between different driving styles (Type 1, Type 2, Type 3), their corresponding true weight parameters $\theta_{i,\text{true}}$, the average weights $\theta_{i,\text{ave}}$ associated with generalized driving styles, and the cognitive threshold ($\epsilon_c$). The red dots are $\theta_{i,\text{ave}}$.
  • Figure 3: The cognitive structure of the HV and CAVs in the hypergame. The dotted-line boxes represent different levels of hypergames: zero level, first level, and second level. On the left side, the solid line boxes indicate HV' cognition of the game at each level of the hypergame, while the right side represents CAVs' cognition, denoted by vehicles of the same color. The box pointed to by the arrow indicates the player's overall understanding of the game at a lower-level hypergame within the context of the current higher-level hypergame, as indicated by boxes and arrows of the same color.
  • Figure 4: Illustration of the online scenario with multi-stage trajectory games.
  • Figure 5: The experimental scenario and reference trajectories for each vehicle.
  • ...and 8 more figures

Theorems & Definitions (9)

  • Remark 1
  • Definition 1
  • Remark 2
  • Definition 2
  • Definition 3
  • Definition 4
  • Remark 3
  • Theorem 1
  • proof