The field of autonomous driving and computer vision is rapidly advancing with a focus on improving perception, tracking, and prediction capabilities. Researchers are exploring innovative techniques such as federated learning, temporal aggregation, and geometry-aware networks to enhance the accuracy and robustness of autonomous driving systems. Notable developments include the use of monocular 3D object tracking, real-time road surface reconstruction, and fine-grained spatial-temporal perception for gas leak segmentation. These advancements have the potential to significantly improve the safety and efficiency of autonomous vehicles. Noteworthy papers include S3MOT, which presents a novel selective state space model for monocular 3D object tracking, and FedEMA, which proposes a federated exponential moving averaging framework for autonomous driving. The ATLAS of Traffic Lights paper introduces a reliable perception framework for autonomous driving, and the Geometry-aware Temporal Aggregation Network paper presents a novel network for monocular 3D lane detection.
Advances in Autonomous Driving and Computer Vision
Sources
S3MOT: Monocular 3D Object Tracking with Selective State Space Model
Study on Real-Time Road Surface Reconstruction Using Stereo Vision
LiDAR-Guided Monocular 3D Object Detection for Long-Range Railway Monitoring
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving
3DPyranet Features Fusion for Spatio-temporal Feature Learning
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving
Measuring Train Driver Performance as Key to Approval of Driverless Trains
Geometry-aware Temporal Aggregation Network for Monocular 3D Lane Detection
Occlusion-aware Driver Monitoring System using the Driver Monitoring Dataset
OG-HFYOLO :Orientation gradient guidance and heterogeneous feature fusion for deformation table cell instance segmentation
Enhancing Self-Supervised Fine-Grained Video Object Tracking with Dynamic Memory Prediction
Fine-grained spatial-temporal perception for gas leak segmentation
FedEMA: Federated Exponential Moving Averaging with Negative Entropy Regularizer in Autonomous Driving
iMacSR: Intermediate Multi-Access Supervision and Regularization in Training Autonomous Driving Models
A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic