Planning and decision-making for connected autonomous vehicles at road intersections: A review
Planning and decision-making technology at intersections is a comprehensive research
problem in intelligent transportation systems due to the uncertainties caused by a variety of …
problem in intelligent transportation systems due to the uncertainties caused by a variety of …
[HTML][HTML] Path planning algorithms in the autonomous driving system: A comprehensive review
This comprehensive review focuses on the Autonomous Driving System (ADS), which aims
to reduce human errors that are the reason for about 95% of car accidents. The ADS …
to reduce human errors that are the reason for about 95% of car accidents. The ADS …
Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness
Driving safety is the most important element that needs to be considered for autonomous
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
vehicles (AVs). To ensure driving safety, we proposed a lane change decision-making …
Lane change strategies for autonomous vehicles: A deep reinforcement learning approach based on transformer
End-to-end approaches are one of the most promising solutions for autonomous vehicles
(AVs) decision-making. However, the deployment of these technologies is usually …
(AVs) decision-making. However, the deployment of these technologies is usually …
A temporal–spatial deep learning approach for driver distraction detection based on EEG signals
Distracted driving has been recognized as a major challenge to traffic safety improvement.
This article presents a novel driving distraction detection method that is based on a new …
This article presents a novel driving distraction detection method that is based on a new …
Bi-level convex optimization of eco-driving for connected Fuel Cell Hybrid Electric Vehicles through signalized intersections
Eco-driving for connected Fuel Cell Hybrid Electric Vehicles (FCHEVs) is a coupled problem
of speed planning and energy management. To reduce the computational burden, bi-level …
of speed planning and energy management. To reduce the computational burden, bi-level …
Interaction-aware trajectory prediction and planning for autonomous vehicles in forced merge scenarios
Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …
especially in dense traffic, because the merging vehicle typically needs to interact with other …
SIND: A drone dataset at signalized intersection in China
Y Xu, W Shao, J Li, K Yang, W Wang… - 2022 IEEE 25th …, 2022 - ieeexplore.ieee.org
Intersection is one of the most challenging scenarios for autonomous driving tasks. Due to
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …
the complexity and stochasticity, essential applications (eg, behavior modeling, motion …
Reinforcement learning-based autonomous driving at intersections in CARLA simulator
R Gutiérrez-Moreno, R Barea, E López-Guillén… - Sensors, 2022 - mdpi.com
Intersections are considered one of the most complex scenarios in a self-driving framework
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …
due to the uncertainty in the behaviors of surrounding vehicles and the different types of …
Adaptive speed planning of connected and automated vehicles using multi-light trained deep reinforcement learning
Through shared real-time traffic information and perception of complex environments,
connected and automated vehicles (CAVs) are endowed with global decision-making …
connected and automated vehicles (CAVs) are endowed with global decision-making …