Safety

In developing countries, the growing use of autos is creating a clash with pedestrians. Pedestrian-vehicle collisions clearly have immediate negative consequences on public health. In the longer run, if walking is not safe, comfortable, and convenient, it is likely to decline as a mode of travel in countries where households and individuals increasingly have the means to choose alternatives. Even so, most people will continue to walk for many trips, and with increasing motorization will be at risk of harm unless the safety and comfort of the walking environment is improved. We use data...

This paper develops an optimization-based control framework for a microscopic nonlinear car-following model. The controller is obtained from a Control Lyapunov Function-Control Barrier Function-Quadratic Programming framework that enforces stability, velocity feasibility, and collision-avoidance constraints while minimizing control effort. The resulting controller mitigates the limitations of the spacing-dependent singularity-based car-following models and guarantees closed-loop safety and stability.

Objectives: To examine changes in alcohol-involved traffic crash rates among young drivers in California during and following the COVID-19 pandemic.
Methods: We conducted a cross-sectional study of alcohol-involved crashes among drivers aged 15–24 using data from the University of California, Berkeley Transportation Injury Mapping System (2016–2024). The primary exposure was COVID-19 policy period (pre-, during-, and post-pandemic). Annual crash rates per 100,000 population were calculated, and Quasi-Poisson regression models estimated rate ratios (RRs) comparing COVID-19 periods,...

Identifying safety-critical scenarios is essential for autonomous driving, but the rarity of such events makes supervised labeling impractical. Traditional rule-based metrics like Time-to-Collision are too simplistic to capture complex interaction risks, and existing methods lack a systematic way to verify whether statistical anomalies truly reflect physical danger. To address this gap, we propose an unsupervised anomaly detection framework based on a multi-agent Transformer that models normal driving and measures deviations through prediction residuals. A dual evaluation scheme has been...