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Found 13 papers, 4 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.
MonoOcc: Digging into Monocular Semantic Occupancy Prediction
However, existing methods rely on a complex cascaded framework with relatively limited information to restore 3D scenes, including a dependency on supervision solely on the whole network's output, single-frame input, and the utilization of a small backbone.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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