Survey of deep reinforcement learning for motion planning of autonomous vehicles
S Aradi - IEEE Transactions on Intelligent Transportation …, 2020 - ieeexplore.ieee.org
Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …
recent years related to several topics as sensor technologies, V2X communications, safety …
Augmenting reinforcement learning with transformer-based scene representation learning for decision-making of autonomous driving
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 …
interactive traffic participants and the complexity of road structures. Although reinforcement …
[HTML][HTML] A hybrid motion planning framework for autonomous driving in mixed traffic flow
As a core part of an autonomous driving system, motion planning plays an important role in
safe driving. However, traditional model-and rule-based methods lack the ability to learn …
safe driving. However, traditional model-and rule-based methods lack the ability to learn …
[HTML][HTML] Design of a reinforcement learning-based lane keeping planning agent for automated vehicles
Featured Application The presented method can be used as a real-time trajectory following
algorithm for autonomous vehicles using prediction based on lookahead information …
algorithm for autonomous vehicles using prediction based on lookahead information …
Hierarchical evasive path planning using reinforcement learning and model predictive control
Motion planning plays an essential role in designing self-driving functions for connected and
autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to …
autonomous vehicles. The methods need to provide a feasible trajectory for the vehicle to …
Motion planning for autonomous vehicles in the presence of uncertainty using reinforcement learning
K Rezaee, P Yadmellat… - 2021 IEEE/RSJ …, 2021 - ieeexplore.ieee.org
Motion planning under uncertainty is one of the main challenges in developing autonomous
driving vehicles. In this work, we focus on the uncertainty in sensing and perception, resulted …
driving vehicles. In this work, we focus on the uncertainty in sensing and perception, resulted …
Comfort-oriented motion planning for automated vehicles using deep reinforcement learning
N Rajesh, Y Zheng, B Shyrokau - IEEE Open Journal of …, 2023 - ieeexplore.ieee.org
Automated vehicles promise numerous advantages to their users. The proposed benefits
could however be overshadowed by a rise in the susceptibility of passengers to motion …
could however be overshadowed by a rise in the susceptibility of passengers to motion …
[HTML][HTML] Online trajectory planning with reinforcement learning for pedestrian avoidance
Planning the optimal trajectory of emergency avoidance maneuvers for highly automated
vehicles is a complex task with many challenges. The algorithm needs to decrease accident …
vehicles is a complex task with many challenges. The algorithm needs to decrease accident …
A Review of Reward Functions for Reinforcement Learning in the context of Autonomous Driving
A Abouelazm, J Michel, JM Zoellner - arXiv preprint arXiv:2405.01440, 2024 - arxiv.org
Reinforcement learning has emerged as an important approach for autonomous driving. A
reward function is used in reinforcement learning to establish the learned skill objectives …
reward function is used in reinforcement learning to establish the learned skill objectives …
TOFG: Temporal occupancy flow graph for prediction and planning in autonomous driving
In autonomous driving, an accurate understanding of the environment, eg, the vehicle-to-
vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks, such as …
vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks, such as …