Connected and automated vehicles (CAVs) can improve transportation safety and efficiency based on vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications. However, there are potential cyber threats to the communication systems via on-board unit equipped in CAVs and road-side unit. Based on existing traffic flow models, we design an evaluation framework for cyber-attacks on CAVs, and further investigate the impact of proportion of cyber-attacked vehicles, cyber-attack severity, cyber-attack range and traffic demand. Moreover, performance of the transportation system is analysed based on four indicators, including efficiency, safety, emissions and fuel consumption. The numerical simulation results show that with the increase of cyber-attacked vehicles and higher cyber-attack severity, the negative impact on traffic flow gradually becomes notable with lower capacity, higher risk of rear-end collision, more air pollutants and fuel consumption. In addition, it may lead to accidents and inefficient traffic operations if cyber-attacks occur on position rather than speed, and thus the position-attacked traffic system consumes more energy and emits more pollutants. The findings of this study provide useful information for the prediction of future cyber-attacked traffic, comprehensive evaluation of transportation systems, as well as management of automated highway systems from the perspective of network security.

This paper proposes three route control strategies for autonomous vehicles (AVs) exiting to off-ramps. First, two microscopic lane-changing models are developed to simulate the off-ramp behaviors for AVs. A risk factor λ is introduced in the discretionary lane-changing (DLC) model, and a five-step process is developed for the mandatory lane-changing (MLC) model. Second, three route control strategies are presented, i.e., grading strategy (GS), half-overlap strategy (HOS), and full-overlap strategy (FOS). Finally, the extensive numerical simulations are conducted to analyze the characteristics of each strategy based on the MATLAB platform. The results indicate that the studied area displays significant variability in spatiotemporal dynamics of average velocity and road capacity under different strategies. It is found that the GS is the most effective control strategy for traffic congestion dissipation and travel delay reduction, followed by the HOS and, then, the FOS. The road capacity under the GS is increased by 19.1% and 7.8% compared to the FOS and the HOS, respectively. Furthermore, the GS consumes lower costs in terms of safety, travel efficiency, and route and lane switch frequency compared to the HOS and FOS, which is verified as the optimal strategy according to overall running cost. The findings of this paper can provide useful references for the design of AVs as well as the management of the automated highway systems.

With the development of Connected Autonomous Vehicle (CAV) technology, different information flow topologies (IFTs) have been applied to CAV Ad Hoc Networks. Firstly, from the perspective of the controller, a general model is proposed to directly reflect the actual communication effect on the controller instead of simply abstracting it into the optimal time interval, which is more feasible. Secondly, linear stability analysis is carried out based on the general model where different time delays are considered, and stability criterion is obtained for subsequent analysis. Finally, we compare the three main IFTs through numerical simulations and analyze the difference in stability region, robustness, traffic safety, and Eco-driving between platoon Cooperative Adaptive Cruise Control (CACC) controllers based on different IFTs. It is found that adopting CACCs can notably improve the traffic capacity and traffic safety relative to Manual Vehicle (MV) no matter what IFT is adopted. As for the difference between the three IFTs, predecessor-leader following (PLF) and multiple-predecessor-leader following (MPLF) are significantly superior to predecessor following (PF), while the choice between PLF and MPLF depends on the communication bandwidth. With higher communication bandwidth or fewer communication vehicles, MPLF will be a better option; on the contrary, PLF is more suitable.

In highway on-ramp sections, the conflictual interactions between a subject vehicle (merging vehicle) in the acceleration lane and a following vehicle (lagging vehicle) in the adjacent mainline can lead to traffic congestion, go–stop oscillations, and serious safety hazards. Human drivers combine their previous lane-changing experience and their perception of surrounding traffic conditions to decide whether to merge. However, the decisions that they make are not always optimal in specific traffic scenarios due to fuzzy perception and misjudgment. That is, they make lane-changing decisions in a bounded rational way. In this paper, a game-theory-based approach is used to model the interactive behavior of mandatory lane-changing in a highway on-ramp section. The model comprehensively considers vehicle interactions and the bounded rationality of drivers by modeling lane-changing behavior on on-ramps as a two-person non-zero-sum non-cooperative game with incomplete information. In addition, the Logit QRE is used to explain the bounded rationality of drivers. In order to estimate the parameters, a bi-level programming framework is built. Vehicle trajectory data from NGSIM and an unmanned aerial vehicle survey were used for model calibration and validation. The validation results were rigorously evaluated by using various performance indicators, such as the mean absolute error, root mean square error, detection rate, and false-alarm rate. It can be seen that the proposed game theory-based model was able to effectively predict merging and yielding interactions with a high degree of accuracy.