N Raju, H Farah - Journal of Advanced Transportation, 2021 - Wiley Online Library
Traffic microsimulation has a functional role in understanding the traffic performance on the road network. This study originated with intent to understand traffic microsimulation and its …
Efficient reasoning about the semantic, spatial, and temporal structure of a scene is a crucial prerequisite for autonomous driving. We present NEural ATtention fields (NEAT), a novel …
End-to-end autonomous driving has great potential in the transportation industry. However, the lack of transparency and interpretability of the automatic decision-making process …
This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more …
Z Gu, L Gao, H Ma, SE Li, S Zheng… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Reinforcement learning (RL) has shown excellent performance in the sequential decision- making problem, where safety in the form of state constraints is of great significance in the …
J Hu, Y Zhang, S Rakheja - IEEE Transactions on Intelligent …, 2022 - ieeexplore.ieee.org
This paper proposes an adaptive lane change trajectory planning scheme to road friction and vehicle speed for autonomous driving, while considering both the maneuver safety and …
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures. Although reinforcement …
J Wang, T Zhang, N Ma, Z Li, H Ma… - IET Cyber‐Systems …, 2021 - Wiley Online Library
A fundamental task in robotics is to plan collision‐free motions among a set of obstacles. Recently, learning‐based motion‐planning methods have shown significant advantages in …
T Zhang, J Zhan, J Shi, J Xin… - IEEE/CAA Journal of …, 2023 - ieeexplore.ieee.org
With the maturation of autonomous driving technology, the use of autonomous vehicles in a socially acceptable manner has become a growing demand of the public. Human-like …