A Functional Co-Design towards Safe and Secure Vehicle Platooning

Abstract

Cooperative adaptive cruise control (CACC) or platooning recently becomes promising as vehicles can learn of nearby vehicles' intentions and dynamics through wireless vehicle to vehicle (V2V) communication and advanced on-board sensing technologies. The complexity of automated vehicle platoon system opens doors to various malicious cyber attacks. Violation of cybersecurity often results in serious safety issues as been demonstrated in recent studies. However, safety and security in a vehicle platoon so far have been considered separately by different sets of experts. Consequently no existing solution solves both safety and security in a coherent way. In this paper, we show cyber attacks on an automated platoon system could have the most severe level of safety impact with large scale car crash and argue the importance of safety-security co-design for safety critical cyber physical systems (CPS). We propose a safety-security co-design engineering process to derive functional security requirements for a safe automated vehicle platoon system based on a deep comprehension on the interrelation of safety and security. To our best knowledge, we are the first to apply the safety-security co-design concept to a concrete application. Through this engineering process, we propose a general approach for designing a safe and secure platooning. Following the general approach, we come up with a new platoon control algorithm that takes into account both safety and security. Our defense mechanism implicitly defends against safety-related cyber-attacks and greatly shortens the safe distance required when the platoon is not protected. viii

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

“…A dangerous circumstance can occur. For example, crashing accidents from the rear end can occur if, for instance, recent studies [140], [141] prove that in a CACC setting, feeding false data to a wireless conduit can cause a malevolent car to increase or reduce the speed of other vehicles incorrectly.…”

Secure State Estimation for Motion Monitoring of Intelligent Connected Vehicle Systems

“…There are many kinds of cyber-attack such as denial of service (DoS), interference attack, false data injection attack and so on [21][22][23]. In this section we consider the false data injection attack.…”

“…We can obtain the estimation Ê by computing Equation (24). Clearly, this approximation greatly reduces the computing burden of solving the estimation of the attack vector E. Moreover, substituting Ê into (21) and simplifying the equation, we derive the initial state x(0) by:…”

“…Dadras et al [20] studied the ability of an attacker to invade a networked vehicle by remote attack and showed that attackers can remotely control the individual position and speed of networked vehicles. Liu et al [21] showed the serious impacts of the cyber-attack on automated platoon systems and proposed a design approach for safe platooning controllers. Following the method in [21], the safe inter-vehicle distance is greatly shortened.…”

“…Liu et al [21] showed the serious impacts of the cyber-attack on automated platoon systems and proposed a design approach for safe platooning controllers. Following the method in [21], the safe inter-vehicle distance is greatly shortened. Alipour-Fanid et al [22] conducted a comprehensive analysis of stability and safety for vehicle strings over wireless Rician fading channels under jamming attacks.…”