6 accepted papers
Semantic segmentation is crucial for autonomous navigation in off-road environments, enabling precise classification of surroundings to identify traversable regions. However, distinctive factors inherent to off-road conditions, such as source-target domain discrepancies and sensor corruption from ro…
Traversability estimation is a core function for robot navigation in off-road unstructured environments and diverse research results have been published so far. One of the recent approaches is using the self-supervised learning (SSL) technique. SSL has been focused on as a breakthrough technique for
Deep reinforcement learning has been used to solve complex tasks in various fields, particularly in robotics control. However, agents trained using deep reinforcement learning have a problem of taking overconfident actions, even when the input state is far from the learned state distribution. This r
Autonomous vehicles must be able to understand the surrounding traffic flows and predict the future traffic conditions for planning a safe maneuver. During prediction, the action of autonomous vehicles should be considered, as it influences the interaction between vehicles sharing the same traffic s…
Autonomous driving with imitation learning is vulnerable to the quality of an expert dataset. Typical driving involves situations or online data that are biased toward specific scenarios such as lane following or stop. This property causes an imbalance in the driving dataset, and it is highly likely…
How to explore environments is one of the most critical factors for the performance of an agent in reinforcement learning. Conventional exploration strategies such as ε-greedy algorithm and Gaussian exploration noise simply depend on pure randomness. However, it is required for an agent to consider…