Complexity is a key measure of driving scenario significance for scenario-based autonomous driving tests. However, current methods for quantifying scenario complexity primarily focus on static scenes rather than dynamic scenarios and fail to represent the dynamic evolution of scenarios. Autonomous vehicle performance may vary significantly across s...
With over 1.6 million traffic deaths in 2016, automated vehicles equipped with automated driving systems (ADSs) have the potential to increase traffic safety by assuming human driving tasks within the operational design domain (ODD). However, safety validation is challenging due to the open-context problem. Current strategies, such as pure driving ...
Alarm sounds significantly influence a user’s sensory perception while driving, directly affecting driving judgement and safety. Personal experience and the environment play an important role in information cognition, but they are rarely considered in the current warning design. We propose a methodology enabling engineers and designers to locally o...
Accurate 3D object detection is essential for autonomous driving, yet traditional LiDAR models often struggle with sparse point clouds. We propose perspective-aware hierarchical vision transformer-based LiDAR-camera fusion (PLC-Fusion) for 3D object detection to address this. This efficient, multi-modal 3D object detection framework integrates LiDA...
The purpose of this study is to gain a better understanding of the difficulty in measuring consumer acceptance of emergent technologies where artificial intelligence is present in autonomous vehicles (AVs). Using the Technology Acceptance Model (TAM) as our theoretical lens, survey data of US adult consumers are used to better understand consumer a...
To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated a point cloud processing module ut...
The increasing presence of autonomous vehicles (AVs) in transportation, driven by advances in AI and robotics, requires a strong focus on safety in mixed-traffic environments to promote sustainable transportation systems. This study analyzes AV crashes in California using advanced machine learning to identify patterns among various crash factors. T...
Perception systems for assisted driving and autonomy enable the identification and classification of objects through a concentration of sensors installed in vehicles, including Radio Detection and Ranging (RADAR), camera, Light Detection and Ranging (LIDAR), ultrasound, and HD maps. These sensors ensure a reliable and robust navigation system. Rada...
As autonomous driving technology and four-independent-wheel chassis systems advance, four-independent-wheel autonomous vehicles have increasingly become a focal area of modern research. The longitudinal control problem for four-independent-wheel autonomous vehicles presents challenges such as complex models, high nonlinearity, and strong system unc...
Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. The first step is to create a highway traffic environment where the agent can b...
