Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered

The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.

Simulation-Based Evaluation of Variation in Left-Turn Paths in the Coordinated Intersection Management

Coordinated intersection management (CIM) has gained more attention with the advance of connected and autonomous vehicle technology. The optimization of passing schedules and conflict separation between conflicting vehicles are usually conducted based on the predefined travelling paths through the intersection area in the CIM. In real-world implementation, however, the diversity of turn paths exists due to multiple factors such as various vehicle sizes and automation control algorithms. The aim of this paper is to investigate how the variation in left-turn paths affects the feasibility and viability of optimal passing schedules, as well as the safety and efficiency of intersection operation. To do this, we start with identifying six typical left-turn paths to represent the variation. A scenario-based simulation is first conducted by using each of the paths as the nominal path. The optimal schedules and the corresponding alternative schedules are generated to calculate indicators for nominal performance, average performance, and robustness. The best path is selected in terms of schedule optimality and robustness. With schedules obtained by solving CIM models using the selected path, the left-turning CAVs are assumed to travel along one of the six paths randomly to simulate the path divergence. A surrogate safety measure, PET, is utilized to assess the safety of the intersection under CIM. The theoretical PET with the nominal path and the actual PET with the random path are calculated for each conflict event. Comparisons of two PET sets show the increase in conflict risk and vehicle delay. The conclusion can be drawn that the variation in left-turn paths causes the decline in safety level and travelling efficiency and should be considered in the CIM model to ensure safe and efficient implementation in the intersection.

A Priority-Based Autonomous Intersection Management (AIM) Scheme for Connected Automated Vehicles (CAVs)

In this paper, we investigate the intersection traffic management for connected automated vehicles (CAVs). In particular, a decentralized autonomous intersection management scheme that takes into account both the traffic efficiency and scheduling flexibility is proposed, which adopts a novel intersection–vehicle model to check conflicts among CAVs in the entire intersection area. In addition, a priority-based collision-avoidance rule is set to improve the performance of traffic efficiency and shorten the delays of emergency CAVs. Moreover, a multi-objective function is designed to obtain the optimal trajectories of CAVs, which considers ride comfort, velocities of CAVs, fuel consumption, and the constraints of safety, velocity, and acceleration. Simulation results demonstrate that our proposed scheme can achieve good performance in terms of traffic efficiency and shortening the delays of emergency CAVs.

Alleviating Road Traffic Congestion with Artificial Intelligence

This paper reviews current AI solutions towards road traffic congestion alleviation. Three specific AI technologies are discussed, (1) intersection management protocols for coordinating vehicles through a roads intersection in a safe and efficient manner, (2) road pricing protocol that induce optimized traffic flow, and (3) partial or full autonomous driving that can stabilize traffic flow and mitigate adverse traffic shock waves. The paper briefly presents the challenges affiliated with each of these applications along with an overview of state-of-the-art solutions. Finally, real-world implementation gaps and challenges are discussed.