Design and Analysis of Delay-Tolerant Intelligent Intersection Management

Abstract

The rapid development of vehicular network and autonomous driving technologies provides opportunities to significantly improve transportation safety and efficiency. One promising application is centralized intelligent intersection management, where an intersection manager accepts requests from approaching vehicles (via vehicle-to-infrastructure communication messages) and schedules the order for those vehicles to safely crossing the intersection. However, communication delays and packet losses may occur due to the unreliable nature of wireless communication or malicious security attacks (e.g., jamming and flooding), and could cause deadlocks and unsafe situations. In our previous work, we considered these issues and proposed a delay-tolerant intersection management protocol for intersections with a single lane in each direction. In this work, we address key challenges in efficiency and deadlock when there are multiple lanes from each direction, and propose a delay-tolerant protocol for general multi-lane intersection management. We prove that this protocol is deadlock free, safe, and satisfies the liveness property. Furthermore, we extend the traffic simulation suite SUMO with communication modules, implement our protocol in the extended simulator, and quantitatively analyze its performance with the consideration of communication delays. Finally, we also model systems that use smart traffic lights with various back-pressure scheduling methods in SUMO, including the basic back-pressure control, the capacity-aware back-pressure control, and the adaptive max-pressure control. We then compare our delay-tolerant intelligent intersection protocol with smart traffic lights that use the three back-pressure scheduling methods, in the case of a network of interconnected intersections. Simulation results demonstrate that our approach significant outperforms the smart traffic lights under normal operation (i.e., when the communication delay is not too large).

DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios

“…For the intersection, with the development of autonomous driving and vehicle-to-vehicle communication technology, researchers propose a model for intelligent intersection management and developed it for analysis and simulation. It helps the design of actual vehicle network applications [12]. Another research proposes a delay-tolerant protocol for intelligent intersection management, which proves the importance and effectiveness of using this framework to solve time delay in CAV network applications [13].…”

“…Researchers have proposed a delay-tolerant protocol for intelligent junction management of communication delays and developed a model for analyzing the performance of the protocol [12]. With the help of related models developed by it, we design two same connected intersections with each direction in 100 meters, which is shown in Fig.…”

“…And all the sides of the intersection are set to be 100 meters to simplify time measurement. The intersection control method is used from an intelligent intersection management model [12] which can control CAVs pass through the intersection without deadlock. The model can set the communication distance as the maximum hearing range for the communication protocol and it also has the parameter as the communication interval corresponding to the inter-packet gap.…”

Securing Connected Vehicle Applications with an Efficient Dual Cyber-Physical Blockchain Framework

“…2) Bad mouthing attack: In Fig. 2(b), all vehicles send their current and destination lanes as well as estimated arriving time to a centralized intersection manager, which then decides the passing order and timing of the vehicles, similarly as in [4]. When a trust framework is deployed to secure the communication, an attacker may maliciously report misbehavior to degrade the trust for honest vehicles [19].…”

“…However, cyber and physical attacks targeting these systems can have severe safety implications, causing accidents or disrupting traffic flow. For example, an intelligent intersection management system can experience deadlock if messages have a long transmission delay or get lost under the denial of service attack [3], [4]. Merging in the highway will be more prone to accidents if vehicles get wrong position data of surrounding vehicles under spoofing and false message attacks [5].…”