AVoD: Advanced Verify-on-Demand for Efficient Authentication against DoS Attacks in V2X Communication

Owing to the development of information and communication technology (ICT), autonomous cooperative vehicles are being developed. Autonomous cooperative driving combines vehicle-to-everything (V2X) communication technology in existing autonomous driving and provides safe driving by sharing information between communication entities. However, security factors should be considered during communication. Security Credential Management System (SCMS) has been proposed as one of these elements, but it is vulnerable to denial-of-service (DoS) attacks due to message authentication costs. In congested situations, the number of messages exchanged between vehicles becomes very large. However, the performance of the on-board unit (OBU) is not sufficient to handle huge number of messages, which can lead to a DoS attack. Therefore, a technique to prevent DoS attacks on autonomous cooperative driving vehicles using SCMS has been proposed in this paper. The proposed technique reduces authentication costs by classifying similar messages into multiple categories and authenticating only the first message represented in the group for a unit time. The effectiveness of this technique has been demonstrated by comparing the time it takes to verify huge number of message signatures in each method.

Time Coordination and Collision Avoidance Using Leader-Follower Strategies in Multi-Vehicle Missions

In recent years, the increasing popularity of multi-vehicle missions has been accompanied by a growing interest in the development of control strategies to ensure safety in these scenarios. In this work, we propose a control framework for coordination and collision avoidance in cooperative multi-vehicle missions based on a speed adjustment approach. The overall problem is decoupled in a coordination problem, in order to ensure coordination and inter-vehicle safety among the agents, and a collision-avoidance problem to guarantee the avoidance of non-cooperative moving obstacles. We model the network over which the cooperative vehicles communicate using tools from graph theory, and take communication losses and time delays into account. Finally, through a rigorous Lyapunov analysis, we provide performance bounds and demonstrate the efficacy of the algorithms with numerical and experimental results.

Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication

The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.

Proactive Decision Making for ITS Communication

Demand from different actors for extended connectivity where vehicles can exchange data with other vehicles, roadside infrastructure, and traffic control centers have pushed vehicle manufacturers to invest in embedded solutions, which paves the way towards cooperative intelligent transportation systems (C-ITS). Cooperative vehicles enable the development of an ecosystem of services around them. Due to the heterogeneousness of such services and their specific requirements, as well as the need for network resources optimization for ubiquitous connectivity, it is necessary to combine existing wireless technologies, providing applications with a communication architecture that hides such underlying access technologies specificities. Due to vehicles' high velocity, their connectivity context can change frequently. In such scenario, it is necessary to take into account the short-term prevision about network environment; enabling vehicles proactively manage their communications. This chapter discusses about the use of near future information to proactive decision-making process.