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Found 9 papers, 3 papers with code
Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships.
Tactics2D: A Reinforcement Learning Environment Library with Generative Scenarios for Driving Decision-making
For access to the source code and participation in discussions, visit the official GitHub page for Tactcis2D at https://github. com/WoodOxen/Tactics2D.
Choose Your Simulator Wisely: A Review on Open-source Simulators for Autonomous Driving
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings.
Dream to Adapt: Meta Reinforcement Learning by Latent Context Imagination and MDP Imagination
Meta reinforcement learning (Meta RL) has been amply explored to quickly learn an unseen task by transferring previously learned knowledge from similar tasks.
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops
We introduce a hybrid partitioning method that uses both target set partitioning (TSP) and backreachable set partitioning (BRSP) to overcome a lower bound on estimation error that is present when using BRSP.
Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization
The performance of PEARL$^+$ is validated by solving three safety-critical problems related to robots and AVs, including two MuJoCo benchmark problems.
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning
It is essential for an automated vehicle in the field to perform discretionary lane changes with appropriate roadmanship - driving safely and efficiently without annoying or endangering other road users - under a wide range of traffic cultures and driving conditions.
Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography
This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera.
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning
This paper proposes a driving-policy adaptive safeguard (DPAS) design, including a collision avoidance strategy and an activation function.
