An intelligent security system for autonomous cars based on infrared sensors

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack

Deep-Learning-Based Intelligent Intervehicle Distance Control for 6G-Enabled Cooperative Autonomous Driving

Research on the sixth-generation cellular networks (6G) is gaining huge momentum to achieve ubiquitous wireless connectivity. Connected autonomous vehicles (CAVs) is a critical vertical application for 6G, holding great potentials of improving road safety, road and energy efficiency. However, the stringent service requirements of CAV applications on reliability, latency, and high speed communications will present big challenges to 6G networks. New channel access algorithms and intelligent control schemes for connected vehicles are needed for 6G-supported CAV. In this article, we investigated 6G-supported cooperative driving, which is an advanced driving mode through information sharing and driving coordination. First, we quantify the delay upper bounds of 6G vehicle-to-vehicle (V2V) communications with hybrid communication and channel access technologies. A deep learning neural network is developed and trained for the fast computation of the delay bounds in real-time operations. Then, an intelligent strategy is designed to control the intervehicle distance for cooperative autonomous driving. Furthermore, we propose a Markov chain-based algorithm to predict the parameters of the system states, and also a safe distance mapping method to enable smooth vehicular speed changes. The proposed algorithms are implemented in the AirSim autonomous driving platform. Simulation results show that the proposed algorithms are effective and robust with safe and stable cooperative autonomous driving, which greatly improve the road safety, capacity, and efficiency

Emerging privacy challenges and approaches in CAV systems

The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions