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Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Junjie Zhou, Zhaokun Shen, Xiaofan Wang, Lin Wang

TL;DR

The work tackles unsignalized intersections in mixed autonomy traffic composed of CAVs, CHVs, and HVs by proposing a two-level strategy: a high-level HPQ algorithm for right-of-way allocation and a low-level MPC-based planning/control stack for CAVs. HPQ uses FCFS-based priority queues, four entry-lane queues, and conflict-graph analysis to grant right of way while accounting for vehicle type differences and maintaining O($n$) real-time performance. The low-level design provides four CAV control modes (car following, cruise, waiting, conflict-solving) with integrated path and speed planning via model predictive control and Stanley lateral control, enabling undisturbed mode switching and safe conflict resolution. Extensive simulations in SUMO and PreScan show significant improvements in travel time (up to 65%), fewer halts, and smoother speeds across a range of traffic densities, validating both scalability and safety for mixed autonomy intersections. The results indicate strong potential for practical deployment, with future work addressing communication imperfections and pedestrian interactions.

Abstract

With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs, connected human-driven vehicles (CHVs), and un-connected human-driven vehicles (HVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs, CAVs, and HVs, and systemically investigates the unsignalized intersection management strategy from the upper decision-making level to the lower execution level. The unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs, CHVs, and HVs, is built to design the right of way management for different types of vehicles. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper-level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. Results show that the proposed method can decrease the average travel time by 5% to 65% for different traffic flows compared with the comparative methods. The intersection management strategy captures the real-world balance between efficiency and safety for future intelligent traffic systems.

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

TL;DR

The work tackles unsignalized intersections in mixed autonomy traffic composed of CAVs, CHVs, and HVs by proposing a two-level strategy: a high-level HPQ algorithm for right-of-way allocation and a low-level MPC-based planning/control stack for CAVs. HPQ uses FCFS-based priority queues, four entry-lane queues, and conflict-graph analysis to grant right of way while accounting for vehicle type differences and maintaining O() real-time performance. The low-level design provides four CAV control modes (car following, cruise, waiting, conflict-solving) with integrated path and speed planning via model predictive control and Stanley lateral control, enabling undisturbed mode switching and safe conflict resolution. Extensive simulations in SUMO and PreScan show significant improvements in travel time (up to 65%), fewer halts, and smoother speeds across a range of traffic densities, validating both scalability and safety for mixed autonomy intersections. The results indicate strong potential for practical deployment, with future work addressing communication imperfections and pedestrian interactions.

Abstract

With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs, connected human-driven vehicles (CHVs), and un-connected human-driven vehicles (HVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs, CAVs, and HVs, and systemically investigates the unsignalized intersection management strategy from the upper decision-making level to the lower execution level. The unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs, CHVs, and HVs, is built to design the right of way management for different types of vehicles. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper-level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. Results show that the proposed method can decrease the average travel time by 5% to 65% for different traffic flows compared with the comparative methods. The intersection management strategy captures the real-world balance between efficiency and safety for future intelligent traffic systems.
Paper Structure (23 sections, 22 equations, 29 figures, 4 tables, 2 algorithms)

This paper contains 23 sections, 22 equations, 29 figures, 4 tables, 2 algorithms.

Table of Contents

  1. INTRODUCTION
  2. LITERATURE REVIEW
  3. Unsignalized Autonomous Intersection Management
  4. Mixed Autonomy Traffic Streams Management
  5. DESIGN OF SYSTEM ARCHITECTURE
  6. HPQ RIGHT OF WAY ALLOCATION ALGORITHM
  7. Priority Management Model
  8. Right of Way Management Model
  9. Conditions for CHVs to obtain the right of way
  10. Conditions for CAVs to obtain the right of way
  11. Conditions for HVs to obtain the right of way
  12. VEHICLE PLANNING AND CONTROL ALGORITHM DESIGN
  13. Path Planning Model
  14. Speed Planning Model
  15. Vehicle Control based on Model Predictive Control
  16. ...and 8 more sections

Figures (29)

  • Figure 1: Architecture of unsignalized intersection with mixed CAVs, CHVs, and HVs. The system architecture is mainly composed of two levels. The high level is the HPQ algorithm that assigns the right of way to vehicles at the intersection. The low level consists of vehicle planning and control algorithms for CAVs to safely and efficiently resolve potential trajectory conflicts with HVs.
  • Figure 2: Layout of the typical unsignalized intersection.
  • Figure 3: A typical conflict scenario.
  • Figure 4: Vehicle trajectory conflict relationship diagram at intersections.
  • Figure 5: The whole procedure of vehicle planning and control. Based on the right of way allocation results calculated by the HPQ algorithm and the real-time traffic environment, the control modes of vehicles are calculated by the control modes switching algorithm. Then, motion planning is carried out, in which path planning and speed planning run in parallel to obtain the best lateral and longitudinal control scheme. All these control actions are finally output to the actuator for execution.
  • ...and 24 more figures