The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

Vehicle platooning reduces the safety distance between vehicles and the travel time of vehicles so that it leads to an increase in road capacity and to saving fuel consumption. In Europe, many projects for vehicle platooning are being actively developed, but mostly focus on truck platooning on the highway with a simpler topology than that of the urban road. When an existing vehicle platoon is applied to urban roads, many challenges are more complicated to address than highways. They include complex topology, various routes, traffic signals, intersections, frequent lane change, and communication interference depending on a higher vehicle density. To address these challenges, we propose a distributed urban platooning protocol (DUPP) that enables high mobility and maximizes flexibility for driving vehicles to conduct urban platooning in a decentralized manner. DUPP has simple procedures to perform platooning maneuvers and does not require explicit conforming for the completion of platooning maneuvers. Since DUPP mainly operates on a service channel, it does not cause negative side effects on the exchange of basic safety messages on a control channel. Moreover, DUPP does not generate any data propagation delay due to contention-based channel access since it guarantees sequential data transmission opportunities for urban platooning vehicles. Finally, to address a problem of the broadcast storm while vehicles notify detected road events, DUPP performs forwarder selection using an analytic hierarchy process. The performance of the proposed DUPP is compared with that of ENSEMBLE which is the latest European platooning project in terms of the travel time of vehicles, the lifetime of an urban platoon, the success ratio of a designed maneuver, the external cost and the periodicity of the urban platooning-related transmissions, the adaptability of an urban platoon, and the forwarder selection ratio for each vehicle. The results of the performance evaluation demonstrate that the proposed DUPP is well suited to dynamic urban environments by maintaining a vehicle platoon as stable as possible after DUPP flexibly and quickly forms a vehicle platoon without the support of a centralized node.

While emerging technology for self-driving automation in vehicles progresses rapidly, the transition to an era of roads full of fully connected and automated vehicles (CAVs) may take longer than expected. Until then, it is inevitable that CAVs should coexist and interact with drivers of non-autonomous vehicles (NAVs) in urban roads. During this period of transition, it is critical to provide road safety with the mixed vehicular traffic and uncertainty caused by human drivers. To investigate the issues caused by the coexistence and interaction with humans, we propose to build a component-based and interactive intelligent transportation cyber-physical systems (ITCPS) framework. Our design of the interactive ITCPS framework aims to provide a standardized structure for users by defining core components. The framework is specified by behavior models and interfaces for the desired ITCPS components and is implemented as a form of human and hardware-in-the-loop system. We developed an intersection crossing assistance service and an automatic emergency braking service as an example of practical applications using the framework. To evaluate the framework, we tested its performance to show how effectively it operates while supporting real-time processing. The results indicate that it satisfies the timing requirements of vehicle-to-vehicle (V2V) communication and the limited processing time required for performing the functions of behavior models, even though the traffic volume reaches the road capacity. A case study using statistical analysis is conducted to assess the practical value of the developed experimental environment. The results of the case study validate the reliability among the specified variables for the experiments involving human drivers. It has shown that V2V communication support has positive effects on road safety, including intersection safety, braking events, and perception-reaction time (PRT) of the drivers. Furthermore, V2V communication support and PRT are identified as the important indicators affecting road safety at an un-signalized intersection. The proposed interactive framework is expected to contribute in constructing a comprehensive environment for the urban ITCPS and providing experimental support for the analysis of human behavior in the coexistence environment.

Vehicular networks have emerged as a promising means to mitigate safety hazards in modern transportation systems. On highways, emergency situations associated with vehicles necessitate a reliable media access control (MAC) protocol that can provide timely warnings of possible vehicle collisions. In this paper, we present an edge-assisted cluster-based MAC protocol (ECMAC) for packet dissemination in software-defined vehicular networks. To reduce the control messaging overhead for clustering, ECMAC separates the cluster control plane (i.e., managing cluster formation) from the data plane (i.e., actual data transmission and forwarding) by using a software-defined network controller in a cellular network edge server. For transmitting packets, we design a time-division multiple access (TDMA) schedule algorithm to guarantee a high reliability and a low latency. The TDMA schedule in ECMAC is determined by a joint optimization process in the cellular edge, which is formulated as a binary integer linear programming problem and solved by a heuristic approach based on the divide-and-conquer paradigm. This joint optimization process minimizes the signal interference by jointly considering channel assignment and time slot allocation, thereby ensuring reliable communication. Through extensive simulations, our performance results show that ECMAC improves the successful delivery ratio of emergency packets by at least 25 %, compared with state-of-the-art approaches.