Most environment perception methods in autonomous vehicles rely on deep neural
networks because of their impressive performance. However, neural networks have black …

Over the last decades, deep neural networks (DNNs) have penetrated all fields of science
and the real world. As a result of the lack of quantifiable data and model uncertainty, deep …

Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …

Reinforcement learning (RL) can be used to create a tactical decision-making agent for
autonomous driving. However, previous approaches only output decisions and do not …

Object detection in autonomous cars is commonly based on camera images and Lidar
inputs, which are often used to train prediction models such as deep artificial neural …

A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal
behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on …

Deep reinforcement learning (DRL) enables autonomous vehicles to perform complex
decision making using neural networks. However, previous DRL networks only output …

Multimodality fusion based on deep neural networks (DNN) is a significant method for
intelligent vehicles. The special characteristics of DNN lead to the issue of AI safety and …

Estimating the uncertainty of a neural network plays a fundamental role in safety-critical
settings. In perception for autonomous driving, measuring the uncertainty means providing …

Reinforcement learning (RL) has been widely used in the decision-making of autonomous
vehicles (AVs) in recent studies. However, existing RL methods generally find the optimal …