To improve the cybersecurity of flocking control for connected and automated vehicles (CAVs), this paper proposes a novel resilient flocking control by specifically considering cyber-attack threats on vehicle tracking errors. Using the vehicle tracking error dynamics model, a dual extended Kalman filter (DEKF) is applied to detect cyber-attacks as an unknown constant on vehicle tracking information with noise rejections. To handle the coupling effects between tracking errors and cyber-attacks, the proposed DEKF consists of a tracking error filter (TEF) and a cyber-attack filter (CAF), which are utilized to conduct the prediction and correction of tracking errors alternatively. Whenever an abnormal tracking error is detected, an observer-based resilient flocking control is enabled. Demonstrated by simulation results, the proposed cyber-attack detection method and resilient flocking control design can successfully achieve and maintain the flocking control of multi-CAV systems by rejecting certain cyber-attack threats.

Highly automated vehicles (HAVs) must be rigorously evaluated before they are deployed on public roads. An accelerated evaluation framework was proposed in the literature to test HAVs more efficiently. However, running such a test is challenging due to the fact that some of the generated test cases may be not feasible or realistic. This paper proposes an improved accelerated evaluation framework that combines importance sampling with reachability analysis, so that the feasibility of all test cases are guaranteed, and the risk levels of cases are controlled. The performance of the proposed framework is studied using the unprotected pedestrian crossing scenario. A total od 2689 pedestrian–vehicle interaction events are extracted from open-source video data, and a truncated Gaussian mixture model (TGMM) is developed to describe the pedestrian–vehicle interaction. Simulation results show that the proposed method achieves unbiased crash rate estimation in an accelerated fashion while achieving the aforementioned benefits for test case generation (feasible and at controlled risk level).

Safe and energy-efficient driving of connected and automated vehicles (CAVs) must be influenced by human-driven vehicles. Thus, to properly evaluate the energy impacts of CAVs in a simulation framework, a human driver model must capture a wide range of real-world driving behaviors corresponding to the surrounding environment. This paper formulates longitudinal human driving as an optimal control problem with a state constraint imposed by the vehicle in front. Deriving analytically optimal solutions by employing optimal control theory can capture longitudinal human driving behaviors with low computational burden, and adding the state constraint can assist with describing car-following features while anticipating behaviors of the vehicle in front. We also use on-road testing data collected by an instrumented vehicle to validate the proposed human driver model for stop scenarios at intersections. Results show that vehicle stopping trajectories of the proposed model are well matched with those of experimental data.

In this paper, we address nonlinear moving horizon estimation (NMHE) of vehicle lateral speed, as well as the road friction coefficient, using measured signals from sensors common to modern series-production automobiles. Due to nonlinear vehicle dynamics, a standard nonlinear moving horizon formulation leads to non-convex optimization problems, and numerical optimization algorithms can be trapped in undesirable local minima, leading to incorrect solutions. To address the challenge of non-convex cost functions, we propose an estimator with a two-level hierarchy. At the high level, a grid search combined with numerical optimization aims to find reference estimates that are sufficiently close to the global optimum. The reference estimates are refined at the low level leading to high-precision solutions. Our algorithm ensures that the estimates converge to the true values for the nominal model without the need for accurate initialization. Our design is tested in simulation with both the nominal model as well as a high-fidelity model of Autonomoose, the self-driving car of the University of Waterloo.