A survey on imitation learning techniques for end-to-end autonomous vehicles
The state-of-the-art decision and planning approaches for autonomous vehicles have
moved away from manually designed systems, instead focusing on the utilisation of large …
moved away from manually designed systems, instead focusing on the utilisation of large …
Microscopic traffic simulation by cooperative multi-agent deep reinforcement learning
G Bacchiani, D Molinari, M Patander - arXiv preprint arXiv:1903.01365, 2019 - arxiv.org
Expert human drivers perform actions relying on traffic laws and their previous experience.
While traffic laws are easily embedded into an artificial brain, modeling human complex …
While traffic laws are easily embedded into an artificial brain, modeling human complex …
Dynamic interaction-aware scene understanding for reinforcement learning in autonomous driving
The common pipeline in autonomous driving systems is highly modular and includes a
perception component which extracts lists of surrounding objects and passes these lists to a …
perception component which extracts lists of surrounding objects and passes these lists to a …
Inverse reinforcement learning with hybrid-weight trust-region optimization and curriculum learning for autonomous maneuvering
Despite significant advancements, collision-free navigation in autonomous driving is still
challenging, considering the navigation module needs to balance learning and planning to …
challenging, considering the navigation module needs to balance learning and planning to …
Interaction-Aware Planning With Deep Inverse Reinforcement Learning for Human-Like Autonomous Driving in Merge Scenarios
J Nan, W Deng, R Zhang, Y Wang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Merge scenarios on highway are often challenging for autonomous driving, due to its lack of
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …
sufficient tacit understanding on and subtle interaction with human drivers in the traffic flow …
Exploring imitation learning for autonomous driving with feedback synthesizer and differentiable rasterization
We present a learning-based planner that aims to robustly drive a vehicle by mimicking
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …
human drivers' driving behavior. We leverage a mid-to-mid approach that allows us to …
Hierarchical reinforcement learning for dynamic autonomous vehicle navigation at intelligent intersections
Recent years have witnessed the rapid development of the Cooperative Vehicle
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …
Infrastructure System (CVIS), where road infrastructures such as traffic lights (TL) and …
Socially compliant navigation through raw depth inputs with generative adversarial imitation learning
We present an approach for mobile robots to learn to navigate in dynamic environments with
pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt …
pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt …
Personalized car following for autonomous driving with inverse reinforcement learning
Driving automation is gradually replacing human driving maneuvers in different applications
such as adaptive cruise control and lane keeping. However, contemporary driving …
such as adaptive cruise control and lane keeping. However, contemporary driving …
Learning automated driving in complex intersection scenarios based on camera sensors: A deep reinforcement learning approach
Making proper decisions at intersections that are one of the most dangerous and
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …
sophisticated driving scenarios is full of challenges, especially for autonomous vehicles …