Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.
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.
Superseding HTTP/1.1, the dominating web protocol, HTTP/2 promises to make web applications faster and safer by introducing many new features, such as multiplexing, header compression, request priority, server push, etc. Although a few recent studies examined the adoption of HTTP/2 and evaluated its impacts, little is known about whether the popular HTTP/2 servers have correctly realized the new features and how the deployed servers use these features. To fill in the gap, in this paper, we conduct the first systematic investigation by inspecting six popular implementations of HTTP/2 servers (i.e., Nginx, Apache, H2O, Lightspeed, nghttpd and Tengine) and measuring the top 1 million Alexa web sites. In particular, we propose new methods and develop a tool named H2Scope to assess the new features in those servers. The results of the largescale measurement on HTTP/2 web sites reveal new observations and insights. This study sheds light on the current status and the future research of HTTP/2.
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