A Survey on platoon-based vehicular cyber-physical systems

Vehicles on the road with some common interests can cooperatively form a platoon-based driving pattern, in which a vehicle follows another one and maintains a small and nearly constant distance to the preceding vehicle. It has been proved that, compared to driving individually, such a platoon-based driving pattern can significantly improve the road capacity and energy efficiency. Moreover, with the emerging vehicular adhoc network (VANET), the performance of platoon in terms of road capacity, safety and energy efficiency, etc., can be further improved. On the other hand, the physical dynamics of vehicles inside the platoon can also affect the performance of VANET. Such a complex system can be considered as a platoon-based vehicular cyber-physical system (VCPS), which has attracted significant attention recently. In this paper, we present a comprehensive survey on platoon-based VCPS. We first review the related work of platoon-based VCPS. We then introduce two elementary techniques involved in platoon-based VCPS: the vehicular networking architecture and standards, and traffic dynamics, respectively. We further discuss the fundamental issues in platoon-based VCPS, including vehicle platooning/clustering, cooperative adaptive cruise control (CACC), platoon-based vehicular communications, etc., and all of which are characterized by the tight coupled relationship between traffic dynamics and VANET behaviors. Since system verification is critical to VCPS development, we also give an overview of VCPS simulation tools. Finally, we share our view on some open issues that may lead to new research directions

An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles

Due to the complexity of the natural world, a programmer cannot foresee all possible situations, a connected and autonomous vehicle (CAV) will face during its operation, and hence, CAVs will need to learn to make decisions autonomously. Due to the sensing of its surroundings and information exchanged with other vehicles and road infrastructure, a CAV will have access to large amounts of useful data. While different control algorithms have been proposed for CAVs, the benefits brought about by connectedness of autonomous vehicles to other vehicles and to the infrastructure, and its implications on policy learning has not been investigated in literature. This paper investigates a data driven driving policy learning framework through an agent-based modelling approaches. The contributions of the paper are two-fold. A dynamic programming framework is proposed for in-vehicle policy learning with and without connectivity to neighboring vehicles. The simulation results indicate that while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V) communication of information improves this capability. Furthermore, to overcome the limitations of sensing in a CAV, the paper proposes a novel concept for infrastructure-led policy learning and communication with autonomous vehicles. In infrastructure-led policy learning, road-side infrastructure senses and captures successful vehicle maneuvers and learns an optimal policy from those temporal sequences, and when a vehicle approaches the road-side unit, the policy is communicated to the CAV. Deep-imitation learning methodology is proposed to develop such an infrastructure-led policy learning framework

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

Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)

Cooperative Adaptive Cruise Control (CACC) is one of the driving applications of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and faster transportation through cooperative behavior between vehicles. In CACC, vehicles exchange information, which is relied on to partially automate driving; however, this reliance on cooperation requires resilience against attacks and other forms of misbehavior. In this paper, we propose a rigorous attacker model and an evaluation framework for this resilience by quantifying the attack impact, providing the necessary tools to compare controller resilience and attack effectiveness simultaneously. Although there are significant differences between the resilience of the three analyzed controllers, we show that each can be attacked effectively and easily through either jamming or data injection. Our results suggest a combination of misbehavior detection and resilient control algorithms with graceful degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular Networking Conference (VNC

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This work presents a capacity analysis of Space-Time Block Codes (STBC) for Vehicle-to-Vehicle (V2V) communication in Line-of-Sight (LOS). The aim is to assess how this type of coding performs when the V2V LOS channel is influenced by ground reflections. STBCs of various coding rates are evaluated using antenna elements distributed over the surface of two contiguous vehicles. A multi-ray tracing tool is used to model the multiple constructive/destructive interference patterns of the transmitted/received signals by all pairs of Tx-Rx antenna links. Simulation results show that STBCs are capable of counteracting fades produced by the destructive self-interference components across a range of inter-vehicle distances. Notably, the effectiveness in deep fades is shown to outperform schemes with exclusive receive diversity. Higher-order STBCs with rate losses are also evaluated, showing interesting gains even for low coding rate performance, particularly, when accompanied by a multiple antenna receiver. Overall, these results can shed light on how to exploit transmit diversity in slow fading vehicular channels.info:eu-repo/semantics/publishedVersio

Message dissemination scheduling for multiple cooperative drivings

With the advances of control and vehicular communication technologies, a group of connected and autonomous (CA) vehicles can drive cooperatively to form a so-called cooperative driving pattern, which has been verified to significantly improve road safety, traffic efficiency and the environmental sustainability. A more general scenario that various types of cooperative driving, such as vehicle platooning and traffic monitoring, coexist on roads will appear soon. To support such multiple cooperative drivings, it is critical to design an efficient scheduling algorithm for periodical message dissemination, i.e. beacon, in a shared communication channel, which has not been fully addressed before. In this paper, we consider multiple cooperative drivings in a bidirectional road, and propose both the decentralized and the RSU-assisted centralized beacon scheduling algorithms which aim at guaranteeing reliable delivery of beacon messages for cooperative drivings as well as maximizing the channel utilization. Numerical results confirm the effectiveness of the proposed algorithms

Aerodynamic disturbance on vehicle’s dynamic parameters

This research paper analysed the influence of aerodynamic disturbance on vehicle’s dynamic parameters. The vehicle dynamics were formulated from the Newton’s Second Law for modelling the vehicle. The vehicle was built using rigid body frames, mass and multi-body signal blocks of MapleSim2015 platform. Several vehicle masses were used to produce different vehicle dynamics with respect to the same aerodynamic drag and input force. Our analyses have shown that the mass of each vehicle is inversely proportional to the aerodynamic drag applied to it. At a given set-point of 25 ms-1, the vehicle tracked the given speed exactly in the absence of the drag. However, for the lag in displacement, speed and acceleration were found as 25 m, 17 ms-1 and 0.3 ms-2, respectively in the presence of drag with an average jerk of 45 ms-3. This has provided an interesting insight on the effects of drag on the moving vehicle. The proposed vehicle was subjected to the same control strategy to form a two-vehicle, look-ahead convoy as in conventional type. Improvements in the inter-vehicular spacing of 1.7 m, proper speed track, low acceleration(1.05 ms-2) and a suitable jerk of 0.04 ms-3 were achieved over the entire period (160 s) as compared to conventional vehicle. The proposed vehicle model scores higher accuracy than conventional vehicle on two-vehicle, look-ahead model and it has shown that the proposed model is more comfortable than the conventional one