Identify, estimate and bound the uncertainty of reinforcement learning for autonomous driving
Deep reinforcement learning (DRL) has emerged as a promising approach for developing
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …
more intelligent autonomous vehicles (AVs). A typical DRL application on AVs is to train a …
Stochastic pedestrian avoidance for autonomous vehicles using hybrid reinforcement learning
Ensuring the safety of pedestrians is essential and challenging when autonomous vehicles
are involved. Classical pedestrian avoidance strategies cannot handle uncertainty, and …
are involved. Classical pedestrian avoidance strategies cannot handle uncertainty, and …
Long-tail prediction uncertainty aware trajectory planning for self-driving vehicles
A typical trajectory planner of self-driving vehicles commonly relies on predicting the future
behavior of surrounding obstacles. Recently, deep learning technology has been widely …
behavior of surrounding obstacles. Recently, deep learning technology has been widely …
Road traffic law adaptive decision-making for self-driving vehicles
Self-driving vehicles have their own intelligence to drive on open roads. However, vehicle
managers, eg, government or industrial companies, still need a way to tell these self-driving …
managers, eg, government or industrial companies, still need a way to tell these self-driving …
A controllable agent by subgoals in path planning using goal-conditioned reinforcement learning
The aim of path planning is to search for a path from the starting point to the goal. Numerous
studies, however, have dealt with a single predefined goal. That is, an agent who has …
studies, however, have dealt with a single predefined goal. That is, an agent who has …
Solving combined economic and emission dispatch problems using reinforcement learning-based adaptive differential evolution algorithm
W Luo, X Yu, Y Wei - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Nowadays, economic and environmental concerns in production have become increasingly
significant. To address these issues, the Combined Economic and Emission Dispatch …
significant. To address these issues, the Combined Economic and Emission Dispatch …
Reliable autonomous driving environment model with unified state-extended boundary
From the early stage of robotic applications to current autonomous driving technologies,
environment modeling has been acting as the middleware for connecting perception and …
environment modeling has been acting as the middleware for connecting perception and …
Risk sensitive dead-end identification in safety-critical offline reinforcement learning
In safety-critical decision-making scenarios being able to identify worst-case outcomes, or
dead-ends is crucial in order to develop safe and reliable policies in practice. These …
dead-ends is crucial in order to develop safe and reliable policies in practice. These …
Dynamically conservative self-driving planner for long-tail cases
Self-driving vehicles (SDVs) are becoming reality but still suffer from “long-tail” challenges
during natural driving: the SDVs will continually encounter rare, safety-critical cases that may …
during natural driving: the SDVs will continually encounter rare, safety-critical cases that may …
A hybrid trajectory planning strategy for intelligent vehicles in on-road dynamic scenarios
Efficient trajectory planning for intelligent vehicles in dynamic environments is a non-trivial
task due to the diversity and complexity of driving scenarios. It requires the planner to be …
task due to the diversity and complexity of driving scenarios. It requires the planner to be …