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Intersection movement assist (IMA) is a connected vehicle (CV) application to improve vehicle safety. GPS spoofing attack is one major threat to the IMA application since inaccurate localization results may generate fake warnings that increase rear-end crashes, or cancel real warnings that may lead to angle or swipe crashes. In this work, we first develop a GPS spoofing attack model to trigger the IMA warning of entry vehicles at a roundabout driving scenario. The attack model can generate realistic trajectories while achieving the attack goal. To defend against such attacks, we further design a one-class classifier to distinguish the normal vehicle trajectories from the trajectories under attack. The proposed model is validated with a real-world data set collected from Ann Arbor, Michigan. Results show that although the attack model triggers the IMA warning in a short time (i.e., in a few seconds), the detection model can still identify the abnormal trajectories before the attack succeeds with low false positive and false negative rates. 

Connected Vehicle (CV) technologies are under rapid deployment across the globe and will soon reshape our transportation systems, bringing benefits to mobility, safety, environment, etc. Meanwhile, such technologies also attract attention from cyberattacks. Recent work shows that CV-based Intelligent Traffic Signal Control Systems are vulnerable to data spoofing attacks, which can cause severe congestion effects in intersections. In this work, we explore a general detection strategy for infrastructure-side CV applications by estimating the trustworthiness of CVs based on readily-available infrastructureside sensors. We implement our detector for the CV-based traffic signal control and evaluate it against two representative congestion attacks. Our evaluation in the industrial-grade traffic simulator shows that the detector can detect attacks with at least 95% true positive rates while keeping false positive rate below 7% and is robust to sensor noises. 

Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe planning and navigation. However, few studies have analyzed the adversarial robustness of trajectory prediction or investigated whether the worst-case prediction can still lead to safe planning. To bridge this gap, we study the adversarial robustness of trajectory prediction models by proposing a new adversarial attack that perturbs normal vehicle trajectories to maximize the prediction error. Our experiments on three models and three datasets show that the adversarial prediction increases the prediction error by more than 150%. Our case studies show that if an adversary drives a vehicle close to the target AV following the adversarial trajectory, the AV may make an inaccurate prediction and even make unsafe driving decisions. We also explore possible mitigation techniques via data augmentation and trajectory smoothing. 

Connected vehicle (CV) technology brings both opportunities and challenges to the traffic signal control (TSC) system. While safety and mobility performance could be greatly improved by adopting CV technologies, the connectivity between vehicles and transportation infrastructure may increase the risks of cyber threats. In the past few years, studies related to cybersecurity on the TSC systems were conducted. However, there still lacks a systematic investigation that provides a comprehensive analysis framework. In this study, our aim is to fill the research gap by proposing a comprehensive analysis framework for the cybersecurity problem of the TSC in the CV environment. With potential threats towards the major components of the system and their corresponding impacts on safety and efficiency analyzed, data spoofing attack is considered the most plausible and realistic attack approach. Based on this finding, different attack strategies and defense solutions are discussed. A case study is presented to show the impact of the data spoofing attacks towards a selected CV based TSC system and corresponding mitigation countermeasures. This case study is conducted on a hybrid security testing platform, with virtual traffic and a real V2X communication network. To the best of our knowledge, this is the first study to present a comprehensive analysis framework to the cybersecurity problem of the CV-based TSC systems.