CommonRoad-RL: A configurable reinforcement learning environment for motion planning of autonomous vehicles
Reinforcement learning (RL) methods have gained popularity in the field of motion planning
for autonomous vehicles due to their success in robotics and computer games. However, no …
for autonomous vehicles due to their success in robotics and computer games. However, no …
Probabilistic prediction from planning perspective: Problem formulation, representation simplification and evaluation metric
Accurate probabilistic prediction for intention and motion of road users is a key prerequisite
to achieve safe and high-quality decision-making and motion planning for autonomous …
to achieve safe and high-quality decision-making and motion planning for autonomous …
Safe and Human‐Like Trajectory Planning of Self‐Driving Cars: A Constraint Imitative Method
Safe and human‐like trajectory planning is crucial for self‐driving cars. While model‐based
planning has demonstrated reliability, it is beneficial to incorporate human demonstrations …
planning has demonstrated reliability, it is beneficial to incorporate human demonstrations …
Interactive Motion Planning for Autonomous Vehicles with Joint Optimization
In highly interactive driving scenarios, the actions of one agent greatly influences those of its
neighbors. Planning safe motions for autonomous vehicles in such interactive environments …
neighbors. Planning safe motions for autonomous vehicles in such interactive environments …
Prediction failure risk-aware decision-making for autonomous vehicles on signalized intersections
Motion prediction modules are crucial for autonomous vehicles to forecast the future
behavior of surrounding road users. Failures in prediction modules can mislead a …
behavior of surrounding road users. Failures in prediction modules can mislead a …
Augmented Reinforcement Learning with Efficient Social-Based Motion Prediction for Autonomous Decision-Making
R Gutiérrez-Moreno, C Gómez-Huelamo… - 2023 IEEE 26th …, 2023 - ieeexplore.ieee.org
This paper presents an approach that improves the efficiency and generalization capabilities
of Reinforcement Learning-based autonomous vehicles operating in urban driving …
of Reinforcement Learning-based autonomous vehicles operating in urban driving …
LF-Net: A Learning-based Frenet Planning Approach for Urban Autonomous Driving
Learning-based approaches hold great potential for autonomous urban driving motion
planning. Compared to traditional rule-based methods, they offer greater flexibility in …
planning. Compared to traditional rule-based methods, they offer greater flexibility in …
Contextual recurrent predictive model for long-term intent prediction of vulnerable road users
Recently, the problem of intent and trajectory prediction of vulnerable road users (VRUs) in
urban traffic environments has got some attention from the intelligent transportation research …
urban traffic environments has got some attention from the intelligent transportation research …
Multimodal manoeuvre and trajectory prediction for automated driving on highways using transformer networks
S Mozaffari, MA Sormoli, K Koufos… - IEEE Robotics and …, 2023 - ieeexplore.ieee.org
Predicting the behaviour (ie, manoeuvre/trajectory) of other road users, including vehicles, is
critical for the safe and efficient operation of autonomous vehicles (AVs), aka, automated …
critical for the safe and efficient operation of autonomous vehicles (AVs), aka, automated …
Probabilistic prediction of vehicle semantic intention and motion
Accurately predicting the possible behaviors of traffic participants is an essential capability
for future autonomous vehicles. The majority of current researches fix the number of driving …
for future autonomous vehicles. The majority of current researches fix the number of driving …