[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 …
[HTML][HTML] Review on vehicle detection technology for unmanned ground vehicles
Q Liu, Z Li, S Yuan, Y Zhu, X Li - Sensors, 2021 - mdpi.com
Unmanned ground vehicles (UGVs) have great potential in the application of both civilian
and military fields, and have become the focus of research in many countries. Environmental …
and military fields, and have become the focus of research in many countries. Environmental …
A hierarchical framework for interactive behaviour prediction of heterogeneous traffic participants based on graph neural network
In complex and dynamic urban traffic scenarios, the accurate prediction of trajectories of
surrounding traffic participants (vehicles, pedestrians, etc) with interactive behaviours plays …
surrounding traffic participants (vehicles, pedestrians, etc) with interactive behaviours plays …
Autonomous driving strategies at intersections: Scenarios, state-of-the-art, and future outlooks
Due to the complex and dynamic character of intersection scenarios, the autonomous
driving strategy at intersections has been a difficult problem and a hot point in the research …
driving strategy at intersections has been a difficult problem and a hot point in the research …
Interactive behavior prediction for heterogeneous traffic participants in the urban road: A graph-neural-network-based multitask learning framework
Effectively predicting interactive behaviors of traffic participants in the urban road is the key
to successful decision-making and motion planning of intelligent vehicles. In this article …
to successful decision-making and motion planning of intelligent vehicles. In this article …
A taxonomy and review of algorithms for modeling and predicting human driver behavior
K Brown, K Driggs-Campbell… - arXiv preprint arXiv …, 2020 - arxiv.org
We present a review and taxonomy of 200 models from the literature on driver behavior
modeling. We begin by introducing a mathematical framework for describing the dynamics of …
modeling. We begin by introducing a mathematical framework for describing the dynamics of …
Importance weighted Gaussian process regression for transferable driver behaviour learning in the lane change scenario
Due to advantages of handling problems with nonlinearity and uncertainty, Gaussian
process regression (GPR) has been widely used in the area of driver behaviour modelling …
process regression (GPR) has been widely used in the area of driver behaviour modelling …
Behavior prediction of traffic actors for intelligent vehicle using artificial intelligence techniques: A review
Intelligent vehicle technology has made tremendous progress due to Artificial Intelligence
(AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the …
(AI) techniques. Accurate behavior prediction of surrounding traffic actors is essential for the …
[HTML][HTML] Generalized single-vehicle-based graph reinforcement learning for decision-making in autonomous driving
F Yang, X Li, Q Liu, Z Li, X Gao - Sensors, 2022 - mdpi.com
In the autonomous driving process, the decision-making system is mainly used to provide
macro-control instructions based on the information captured by the sensing system …
macro-control instructions based on the information captured by the sensing system …
A comparative study of deep reinforcement learning-based transferable energy management strategies for hybrid electric vehicles
The deep reinforcement learning-based energy management strategies (EMS) have
become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are …
become a promising solution for hybrid electric vehicles (HEVs). When driving cycles are …