Interaction‐aware autonomous driving (IAAD) is a rapidly growing field of research that
focuses on the development of autonomous vehicles (AVs) that are capable of interacting …

In various examples, a machine learning model—such as a deep neural network (DNN)—
may be trained to use image data and/or other sensor data as inputs to generate two …

Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor
savings. Over the past few years, the number of simulators for autonomous driving has …

Autonomous vehicles (AVs) are expected to reshape future transportation systems, and
decision making is one of the critical modules toward high-level automated driving. To …

This paper addresses learning safe output feedback control laws from partial observations of
expert demonstrations. We assume that a model of the system dynamics and a state …

This paper provides a theoretical understanding of deep Q-Network (DQN) with the
$\varepsilon $-greedy exploration in deep reinforcement learning. Despite the tremendous …

Abstract Inductive Conformal Prediction (ICP) provides a practical and effective approach for
equipping deep learning models with uncertainty estimates in the form of set-valued …

In the field of autonomous driving, two important features of autonomous driving car systems
are the explainability of decision logic and the accuracy of environmental perception. This …

Abstract Automated Vehicles (AVs) are designed and programmed to follow traffic rules.
However, there is no separate and comprehensive regulatory framework dedicated to AVs …

This paper presents the first survey of vehicle dynamics modeling methods for autonomous
racing. Previous surveys have covered dynamics models for standard autonomous vehicles …