An integrated framework of decision making and motion planning for autonomous vehicles considering social behaviors
This paper presents a novel integrated approach to deal with the decision making and
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …
motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social …
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 …
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) …
Addressing inherent uncertainty: Risk-sensitive behavior generation for automated driving using distributional reinforcement learning
J Bernhard, S Pollok, A Knoll - 2019 IEEE Intelligent Vehicles …, 2019 - ieeexplore.ieee.org
For highly automated driving above SAE level 3, behavior generation algorithms must
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
reliably consider the inherent uncertainties of the traffic environment, eg arising from the …
Towards socially responsive autonomous vehicles: A reinforcement learning framework with driving priors and coordination awareness
The advent of autonomous vehicles (AVs) alongside human-driven vehicles (HVs) has
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
ushered in an era of mixed traffic flow, presenting a significant challenge: the intricate …
Cooperative behavior planning for automated driving using graph neural networks
M Klimke, B Völz, M Buchholz - 2022 IEEE Intelligent Vehicles …, 2022 - ieeexplore.ieee.org
Urban intersections are prone to delays and inefficiencies due to static precedence rules
and occlusions limiting the view on prioritized traffic. Existing approaches to improve traffic …
and occlusions limiting the view on prioritized traffic. Existing approaches to improve traffic …
Efficient game-theoretic planning with prediction heuristic for socially-compliant autonomous driving
Planning under social interactions with other agents is an essential problem for autonomous
driving. As the actions of the autonomous vehicle in the interactions affect and are also …
driving. As the actions of the autonomous vehicle in the interactions affect and are also …
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 …
Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning
Autonomous vehicles promise to improve traffic safety while, at the same time, increase fuel
efficiency and reduce congestion. They represent the main trend in future intelligent …
efficiency and reduce congestion. They represent the main trend in future intelligent …
Jointly learnable behavior and trajectory planning for self-driving vehicles
The motion planners used in self-driving vehicles need to generate trajectories that are safe,
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …
comfortable, and obey the traffic rules. This is usually achieved by two modules: behavior …