Predicting vehicle trajectories with inverse reinforcement learning
B Hjaltason - 2019 - diva-portal.org
Autonomous driving in urban environments is challenging because there are many agents
located in the environment all with their own individual agendas. With accurate motion …
located in the environment all with their own individual agendas. With accurate motion …
A Survey of the State-of-the-Art Reinforcement Learning-Based Techniques for Autonomous Vehicle Trajectory Prediction
V Bharilya, N Kumar - 2023 International Conference on …, 2023 - ieeexplore.ieee.org
Autonomous Vehicles (AVs) have emerged as a promising solution by replacing human
drivers with advanced computer-aided decision-making systems. However, for AVs to …
drivers with advanced computer-aided decision-making systems. However, for AVs to …
Learning and adapting behavior of autonomous vehicles through inverse reinforcement learning
The driving behavior of autonomous vehicles has a significant impact on safety for all traffic
participants. Unlike current traffic participants, autonomous vehicles in the future will also …
participants. Unlike current traffic participants, autonomous vehicles in the future will also …
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 …
Retrieving a driving model based on clustered intersection data
In order for autonomous vehicles to learn how to naturally navigate through an intersection,
we present a method of learning from expert drivers using inverse reinforcement learning …
we present a method of learning from expert drivers using inverse reinforcement learning …
A deep reinforcement learning driving policy for autonomous road vehicles
This work regards our preliminary investigation on the problem of path planning for
autonomous vehicles that move on a freeway. We approach this problem by proposing a …
autonomous vehicles that move on a freeway. We approach this problem by proposing a …
Incorporating multi-context into the traversability map for urban autonomous driving using deep inverse reinforcement learning
C Jung, DH Shim - IEEE Robotics and Automation Letters, 2021 - ieeexplore.ieee.org
Autonomous driving in an urban environment with surrounding agents remains challenging.
One of the key challenges is to accurately predict the traversability map that probabilistically …
One of the key challenges is to accurately predict the traversability map that probabilistically …
Utilizing b-spline curves and neural networks for vehicle trajectory prediction in an inverse reinforcement learning framework
MS Jazayeri, A Jahangiri - Journal of Sensor and Actuator Networks, 2022 - mdpi.com
The ability to accurately predict vehicle trajectories is essential in infrastructure-based safety
systems that aim to identify critical events such as near-crash situations and traffic violations …
systems that aim to identify critical events such as near-crash situations and traffic violations …
Vehicle Trajectory Prediction Model Based on Attention Mechanism and Inverse Reinforcement Learning
L Lu, Q Ning, Y Qiu, D Chu - 2022 IEEE 34th International …, 2022 - ieeexplore.ieee.org
Predicting the future trajectory of a vehicle in a dynamic scene is not a simple problem
because the future trajectory of a vehicle is not only influenced by its historical trajectory but …
because the future trajectory of a vehicle is not only influenced by its historical trajectory but …
Learning the Car‐following Behavior of Drivers Using Maximum Entropy Deep Inverse Reinforcement Learning
Y Zhou, R Fu, C Wang - Journal of advanced transportation, 2020 - Wiley Online Library
The present study proposes a framework for learning the car‐following behavior of drivers
based on maximum entropy deep inverse reinforcement learning. The proposed framework …
based on maximum entropy deep inverse reinforcement learning. The proposed framework …