In recent years, developments of visual perception techniques based on deep learning have
significantly promoted the prosperity of autonomous driving in Internet of vehicles scenarios …

Vision is an important sensing modality in automated driving. Deep learning-based
approaches have gained popularity for different computer vision (CV) tasks such as …

One of the aims of the development and spread of autonomous driving technology is to
reduce traffic accidents caused by human factors. But recently reported data on fatal …

As the reliance on virtual simulation scenarios for autonomous driving testing and validation
increases, traditional expert-driven scene enumeration generation methods can no longer …

Testing the safety of the autonomous driving system in dangerous scenarios can effectively
judge its stability and adaptability and improve testing efficiency. Scene parameter sampling …

In order to realize the accelerated test of automatic driving simulation test under the
consideration of scene coverage, this paper proposes an accelerated test method based on …

As shown by recent studies, machine intelligence-enabled systems are vulnerable to test
cases resulting from either adversarial manipulation or natural distribution shifts. This has …

Autonomous Driving Systems (ADSs) are safety-critical systems, and safety violations of
Autonomous Vehicles (AVs) in real traffic will cause huge losses. Therefore, ADSs must be …

This article presents an ongoing research study investigating the potential of using Grand
Theft Auto V (GTAV) as a simulation environment for testing edge cases in self-driving …

Recent advancements in the field of deep learning have enabled its application in
Autonomous Driving Systems (ADS). A Deep Neural Network (DNN) model is often used to …