Connected and Autonomous Vehicles (CAVs) in a platoon can exchange real-time information using Vehicle-to-Vehicle (V2V) communication technology to enhance platoon control performance. However, the V2V communication technology also provides opportunities for cyber-attacks, where falsified information can be injected into vehicle controllers to disrupt the platoon operation and even induce vehicle collisions. To address this problem, this study proposes a robust platoon control strategy for CAVs to mitigate the impacts of the falsified information to maneuver the CAV platoon to achieve consensus safely. The proposed control strategy consists of three components: (i) a ${\mathit{H}}_{\mathrm{\infty }}$ robust control law, which consistently negates the disturbance induced by falsified information; (ii) a state observer which estimates the vehicle states and disturbance induced by falsified information and inputs the estimated results into the ${\mathit{H}}_{\mathrm{\infty }}$ robust control law to compute a synthesized control decision; and (iii) a control decision regulator which applies a Control Barrier Function-based Quadratic Programming (CBF-QP) to regulate the control decision computed by the ${\mathit{H}}_{\mathrm{\infty }}$ robust control law to avoid actuator saturation issue and ensure safe spacing for each vehicle in the platoon. Numerical experiments demonstrate that the proposed control strategy can effectively drive the CAV platoon to the desired consensus safely and efficiently under the impacts of falsified information injection.

Travel time distribution estimation is fundamentally important for the evaluation of travel time variability and reliability. For urban roads, signal delays are key components of travel time. They are stochastic and differ for vehicular movements due to different signal timings for through, left-turning, and right-turning vehicles. To better assist travelers in making trip decisions under connected environments, this study seeks to investigate lane-level travel time distributions for signalized arterial roads by specifically considering the impacts of both the link travel time and the vehicle movement-based signal delays at an intersection. A simulation testbed based on the VISSIM microscopic traffic simulator and a Java plugin is developed to mimic the traffic flow dynamics of a signalized arterial, El Camino Real, in Palo Alto, California, under a connected environment. The detailed vehicle trajectory data obtained from the simulation can be leveraged to obtain lane-level travel time information. Typical normal, lognormal, gamma, and Weibull distributions, as well as kernel density estimation (KDE), are adopted to calibrate the lane-level travel time distributions. The estimation results demonstrate that conventional distribution models are suitable for estimating the travel time distributions of only a few road segments, while KDE fully captures travel time reliability metrics such as the buffer time index, skewness, and width of the travel time distributions for all road segments. This result will help traffic managers and engineers carry out effective traffic management and control to optimize the operation of arterial roads.