Search Results for author:
Found 12 papers, 3 papers with code
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally.
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning
The latent world model provides a promising way to learn policies in a compact latent space for tasks with high-dimensional observations, however, its generalization across diverse environments with unseen dynamics remains challenging.
Model-based Reinforcement Learning
reinforcement-learning
+1
Conditional Goal-oriented Trajectory Prediction for Interacting Vehicles with Vectorized Representation
Our CGTP framework is an end to end and interpretable model, including three main stages: context encoding, goal interactive prediction and trajectory interactive prediction.
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and Feasible Agent Behaviors for Autonomous Driving
In particular, TrajGen consists of the multi-modal trajectory prediction stage and the reinforcement learning based trajectory modification stage.
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic
In this paper, we formulate a multi-task safe reinforcement learning with social attention to improve the safety and efficiency when interacting with other traffic participants.
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection Scenarios
The test benchmark and baselines are to provide a fair and comprehensive training and testing platform for the study of RL for autonomous driving in the intersection scenario, advancing the progress of RL-based methods for intersection autonomous driving control.
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning
We train several state-of-the-art deep reinforcement learning methods in the designed training scenarios and provide the benchmark metrics evaluation results of the trained models in the test scenarios.
Dynamic Horizon Value Estimation for Model-based Reinforcement Learning
Existing model-based value expansion methods typically leverage a world model for value estimation with a fixed rollout horizon to assist policy learning.
Model-based Reinforcement Learning
reinforcement-learning
+1
BiFNet: Bidirectional Fusion Network for Road Segmentation
Considering the bird's eye views(BEV) of the LiDAR remains the space structure in horizontal plane, this paper proposes a bidirectional fusion network(BiFNet) to fuse the image and BEV of the point cloud.
Graph Attention Memory for Visual Navigation
To address the long-term memory issue, this paper proposes a graph attention memory (GAM) architecture consisting of memory construction module, graph attention module and control module.
Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraints
Autonomous driving decision-making is a great challenge due to the complexity and uncertainty of the traffic environment.
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous Driving
The control module which is based on reinforcement learning then makes a control decision based on these features.
