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As autonomous driving technology matures, safety and robustness of its key components, including trajectory prediction, is vital. Though real-world datasets, such as Waymo Open Motion, provide realistic recorded scenarios for model…
Influencing factors on crashes involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses between influencing factors on crashes of AVs and human-driven vehicles. To fill…
Risk mitigation techniques are critical to avoiding accidents associated with driving behaviour. We provide a novel Multi-Class Driver Distraction Risk Assessment (MDDRA) model that considers the vehicle, driver, and environmental data…
In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to…
When a traffic crash occurs, following vehicles need to change lanes to bypass the obstruction. We define these maneuvers as post crash lane changes. In such scenarios, vehicles in the target lane may refuse to yield even after the lane…
Safe navigation in real-time is challenging because engineers need to work with uncertain vehicle dynamics, variable external disturbances, and imperfect controllers. A common safety strategy is to inflate obstacles by hand-defined margins.…
With the emergence of high-frequency connected and automated vehicle data, analysts have become able to extract useful information from them. To this end, the concept of "driving volatility" is defined and explored as deviation from the…
Recent research has found that navigation systems usually assume that all roads are equally safe, directing drivers to dangerous routes, which led to catastrophic consequences. To address this problem, this paper aims to begin the process…
Over 90% of new vehicles in the United States now collect and transmit telematics data. Similar trends are seen in other developed countries. Transportation planners have previously utilized telematics data in various forms, but its current…
The advancement of vehicle automation and the growing adoption of electric vehicles (EVs) are reshaping transportation systems. While fully automated vehicles are expected to improve traffic stability, efficiency, and sustainability, recent…
With the improvements of Los Angeles in many aspects, people in mounting numbers tend to live or travel to the city. The primary objective of this paper is to apply a set of methods for the time series analysis of traffic accidents in Los…
With the rapid development of Connected and Automated Vehicle (CAV) technology, limited self-driving vehicles have been commercially available in certain leading intelligent transportation system countries. When formulating the…
Real-world autonomous driving (AD) especially urban driving involves many corner cases. The lately released AD simulator CARLA v2 adds 39 common events in the driving scene, and provide more quasi-realistic testbed compared to CARLA v1. It…
To operate in open-ended environments where humans interact in complex, diverse ways, autonomous robots must learn to predict their behaviour, especially when that behavior is potentially dangerous to other agents or to the robot. However,…
A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in an urgent need. Risky situations are generally…
This paper 1) analyzes the extent to which drivers engage in multitasking additional-to-driving (MAD) under various conditions, 2) specifies odds ratios (ORs) of crashing associated with MAD compared to no task engagement, and 3) explores…
With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially in unexpected driving scenarios. Although it might be not…
Conventional road safety management is inherently reactive, relying on analysis of sparse and lagged historical crash data to identify hazardous locations, or crash blackspots. The proliferation of vehicle telematics presents an opportunity…
The field of high-speed autonomous racing has seen significant advances in recent years, with the rise of competitions such as RoboRace and the Indy Autonomous Challenge providing a platform for researchers to develop software stacks for…
Traffic collision reconstruction traditionally relies on human expertise and can be accurate, but pre-crash reconstruction is more challenging. This study develops a multi-agent AI framework that reconstructs pre-crash scenarios and infers…