Purpose-Perception has been identified as the main cause underlying most autonomous
vehicle related accidents. As the key technology in perception, deep learning (DL) based …

In the last decade, deep learning (DL) approaches have been used successfully in computer
vision (CV) applications. However, DL-based CV models are generally considered to be …

User trust has been identified as a critical issue that is pivotal to the success of autonomous
vehicle (AV) operations where artificial intelligence (AI) is widely adopted. For such …

Automated driving desires better performance on tasks like motion planning and interacting
with pedestrians in mixed-traffic environments. Deep learning algorithms can achieve high …

This survey reviews explainability methods for vision-based self-driving systems trained with
behavior cloning. The concept of explainability has several facets and the need for …

Current deep learning based autonomous driving approaches yield impressive results also
leading to in-production deployment in certain controlled scenarios. One of the most popular …

Transformer-based models have gained popularity in the field of natural language
processing (NLP) and are extensively utilized in computer vision tasks and multi-modal …

In recent years, the advancement of deep-learning technologies has greatly promoted the
research progress of autonomous driving. However, deep neural network is like a black box …

Current end-to-end deep learning driving models have two problems:(1) Poor generalization
ability of unobserved driving environment when diversity of training driving dataset is limited …

Deep neural perception and control networks are likely to be a key component of self-driving
vehicles. These models need to be explainable-they should provide easy-to-interpret …