Verification and validation methods for decision-making and planning of automated vehicles: A review
Verification and validation (V&V) hold a significant position in the research and development
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …
of automated vehicles (AVs). Current literature indicates that different V&V techniques have …
[HTML][HTML] Continual driver behaviour learning for connected vehicles and intelligent transportation systems: Framework, survey and challenges
Modelling, predicting and analysing driver behaviours are essential to advanced driver
assistance systems (ADAS) and the comprehensive understanding of complex driving …
assistance systems (ADAS) and the comprehensive understanding of complex driving …
A survey on trajectory-prediction methods for autonomous driving
In order to drive safely in a dynamic environment, autonomous vehicles should be able to
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
predict the future states of traffic participants nearby, especially surrounding vehicles, similar …
[HTML][HTML] Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving
Due to its limited intelligence and abilities, machine learning is currently unable to handle
various situations thus cannot completely replace humans in real-world applications …
various situations thus cannot completely replace humans in real-world applications …
CitySim: a drone-based vehicle trajectory dataset for safety-oriented research and digital twins
O Zheng, M Abdel-Aty, L Yue… - Transportation …, 2024 - journals.sagepub.com
The development of safety-oriented research and applications requires fine-grain vehicle
trajectories that not only have high accuracy, but also capture substantial safety-critical …
trajectories that not only have high accuracy, but also capture substantial safety-critical …
Multi-modal motion prediction with transformer-based neural network for autonomous driving
Predicting the behaviors of other agents on the road is critical for autonomous driving to
ensure safety and efficiency. However, the challenging part is how to represent the social …
ensure safety and efficiency. However, the challenging part is how to represent the social …
Differentiable integrated motion prediction and planning with learnable cost function for autonomous driving
Predicting the future states of surrounding traffic participants and planning a safe, smooth,
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
and socially compliant trajectory accordingly are crucial for autonomous vehicles (AVs) …
Efficient deep reinforcement learning with imitative expert priors for autonomous driving
Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous
driving. However, the low sample efficiency and difficulty of designing reward functions for …
driving. However, the low sample efficiency and difficulty of designing reward functions for …
Highway decision-making and motion planning for autonomous driving via soft actor-critic
In this study, a decision-making and motion planning controller with continuous action space
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
is constructed in the highway driving scenario based on deep reinforcement learning. In the …
Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving
Making safe and human-like decisions is an essential capability of autonomous driving
systems, and learning-based behavior planning presents a promising pathway toward …
systems, and learning-based behavior planning presents a promising pathway toward …