Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

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.

• The paper introduces a novel HPQ algorithm that efficiently prioritizes right-of-way for both connected automated and human-driven vehicles.
• It integrates model predictive control for dynamic speed and trajectory planning to optimize vehicle flow at unsignalized intersections.
• Simulation results demonstrate reduced travel times and vehicle halts, with scalable performance across varied traffic densities.

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Introduction to Unsignalized Intersection Management for Mixed Autonomy

This paper addresses a critical area in intelligent transportation systems by proposing an innovative approach to managing unsignalized intersections with mixed traffic comprising connected and automated vehicles (CAVs) and connected human-driven vehicles (CHVs). The discussed strategy integrates system-level decision-making with low-level vehicle control, significantly enhancing traffic efficiency and safety at intersections without traditional signal control mechanisms. The proposed system requires a holistic view due to the coexistence and differences in capabilities between CAVs and CHVs.

Heuristic Priority Queues (HPQ) Algorithm

The core of the management strategy lies in the Heuristic Priority Queues (HPQ) algorithm. This algorithm efficiently allocates right-of-way based on vehicle priority, trajectory conflicts, and vehicle type (CAV or CHV). It is designed to minimize computational overhead while maximizing throughput and safety.

• Priority Management: Priority is assigned on a first-come, first-serve (FCFS) basis, enabling dynamic adjustment in cases of vehicle abnormality, such as breakdowns, which could otherwise cause intersection deadlocks.
• Right of Way Allocation: Vehicles are organized into queues based on their approach lanes, with the right of way granted by assessing conflicts and priority. CAVs can handle certain conflicts better due to their automated nature, thus receiving differentiated treatment accordingly.

Vehicle Planning and Control Algorithm

The vehicle planning and control component focuses on real-time dynamic path and speed planning for CAVs using model predictive control (MPC). The strategy involves:

• Control Modes: Four distinct control modes are implemented—car following, cruising, waiting, and conflict-solving. These modes facilitate a fluid transition between different driving states based on real-time traffic conditions.
• Path and Speed Planning: The use of MPC allows for an optimal balance between speed, safety, and smooth navigation through trajectory conflict points at intersections.

Simulation Results and Performance Analysis

Simulation tests, conducted using a combination of SUMO for macro-level traffic flow and PreScan for micro-level vehicle dynamics, demonstrate the superiority of this intersection management strategy.

• Efficiency and Safety: Compared to baseline algorithms like the Hybrid AIM protocol and delay-time actuated traffic signals, the proposed HPQ algorithm notably reduces average travel time and number of vehicle halts. It also maintains efficient speeds under varying traffic densities.
• Scalability: The strategy scales consistently across various traffic conditions, yielding stable performance and minimal computational demands, making it feasible for near-future deployment.

Conclusion

The paper successfully presents an unsignalized intersection management strategy designed for mixed autonomy traffic streams, emphasizing real-world applicability and future integration prospects. This strategy can be instrumental for urban environments, where transitioning to intelligent traffic systems requires accommodating both CAVs and CHVs. Future research should expand on integrating pedestrian interactions and addressing real-world implementation challenges such as communication delays and environmental factors.