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Found 33 papers, 8 papers with code
V2X-DGPE: Addressing Domain Gaps and Pose Errors for Robust Collaborative 3D Object Detection
V2X-DGPE employs a Knowledge Distillation Framework and a Feature Compensation Module to learn domain-invariant representations from multi-source data, effectively reducing the feature distribution gap between vehicles and roadside infrastructure.
SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for Autonomous Vehicles with Large Language Models
SafeDrive establishes a novel paradigm for integrating knowledge- and data-driven methods, highlighting significant potential to improve safety and adaptability of autonomous driving in high-risk traffic scenarios.
Robust Data-Driven Predictive Control for Mixed Platoons under Noise and Attacks
This leads to a robust data-driven predictive control framework, solved in a tube-based control manner.
EDRF: Enhanced Driving Risk Field Based on Multimodal Trajectory Prediction and Its Applications
However, the probability of events occurring is often difficult to predict due to the uncertainty of drivers' or vehicles' behavior.
Hierarchical End-to-End Autonomous Driving: Integrating BEV Perception with Deep Reinforcement Learning
End-to-end autonomous driving offers a streamlined alternative to the traditional modular pipeline, integrating perception, prediction, and planning within a single framework.
GS-Net: Generalizable Plug-and-Play 3D Gaussian Splatting Module
3D Gaussian Splatting (3DGS) integrates the strengths of primitive-based representations and volumetric rendering techniques, enabling real-time, high-quality rendering.
Vision-Driven 2D Supervised Fine-Tuning Framework for Bird's Eye View Perception
Visual bird's eye view (BEV) perception, due to its excellent perceptual capabilities, is progressively replacing costly LiDAR-based perception systems, especially in the realm of urban intelligent driving.
Passenger hazard perception based on EEG signals for highly automated driving vehicles
Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems.
OE-BevSeg: An Object Informed and Environment Aware Multimodal Framework for Bird's-eye-view Vehicle Semantic Segmentation
Based on prior knowledge about the main composition of the BEV surrounding environment varying with the increase of distance intervals, long-sequence global modeling is utilized to improve the model's understanding and perception of the environment.
Hierarchical and Decoupled BEV Perception Learning Framework for Autonomous Driving
Perception is essential for autonomous driving system.
Neural Radiance Field in Autonomous Driving: A Survey
To the best of our knowledge, this is the first survey specifically focused on the applications of NeRF in the Autonomous Driving domain.
PreGSU-A Generalized Traffic Scene Understanding Model for Autonomous Driving based on Pre-trained Graph Attention Network
In this study, we propose PreGSU, a generalized pre-trained scene understanding model based on graph attention network to learn the universal interaction and reasoning of traffic scenes to support various downstream tasks.
D2E-An Autonomous Decision-making Dataset involving Driver States and Human Evaluation
Although current datasets have made significant progress in the collection of vehicle and environment data, emphasis on human-end data including the driver states and human evaluation is not sufficient.
MergeOcc: Bridge the Domain Gap between Different LiDARs for Robust Occupancy Prediction
LiDAR-based 3D occupancy prediction evolved rapidly alongside the emergence of large datasets.
Cloud Control of Connected Vehicle under Bi-directional Time-varying delay: An Application of Predictor-observer Structured Controller
This article is devoted to addressing the cloud control of connected vehicles, specifically focusing on analyzing the effect of bi-directional communication-induced delays.
Information Flow Topology in Mixed Traffic: A Comparative Study between "Looking Ahead" and "Looking Behind"
In mixed traffic where human-driven vehicles (HDVs) also exist, existing research mostly focuses on "looking ahead" (i. e., the CAVs receive information from preceding vehicles) strategies for CAVs, while recent work reveals that "looking behind" (i. e., the CAVs receive information from their rear vehicles) strategies might provide more possibilities for CAV longitudinal control.
A Survey on Datasets for Decision-making of Autonomous Vehicle
Autonomous vehicles (AV) are expected to reshape future transportation systems, and decision-making is one of the critical modules toward high-level automated driving.
4D Millimeter-Wave Radar in Autonomous Driving: A Survey
In an effort to bridge this gap and stimulate future research, this paper presents an exhaustive survey on the utilization of 4D mmWave radar in autonomous driving.
Lossless Point Cloud Attribute Compression Using Cross-scale, Cross-group, and Cross-color Prediction
With this aim, we extensively exploit cross-scale, cross-group, and cross-color correlations of point cloud attribute to ensure accurate probability estimation and thus high coding efficiency.
Dynamic Point Cloud Geometry Compression Using Multiscale Inter Conditional Coding
This work extends the Multiscale Sparse Representation (MSR) framework developed for static Point Cloud Geometry Compression (PCGC) to support the dynamic PCGC through the use of multiscale inter conditional coding.
CARNet:Compression Artifact Reduction for Point Cloud Attribute
A learning-based adaptive loop filter is developed for the Geometry-based Point Cloud Compression (G-PCC) standard to reduce attribute compression artifacts.
Efficient LiDAR Point Cloud Geometry Compression Through Neighborhood Point Attention
Although convolutional representation of multiscale sparse tensor demonstrated its superior efficiency to accurately model the occupancy probability for the compression of geometry component of dense object point clouds, its capacity for representing sparse LiDAR point cloud geometry (PCG) was largely limited.
Sparse Tensor-based Point Cloud Attribute Compression
Recently, numerous learning-based compression methods have been developed with outstanding performance for the coding of the geometry information of point clouds.
Experimental Validation of Multi-lane Formation Control for Connected and Automated Vehicles in Multiple Scenarios
Formation control methods of connected and automated vehicles have been proposed to smoothly switch the structure of vehicular formations in different scenarios.
Sparse Tensor-based Multiscale Representation for Point Cloud Geometry Compression
We call this compression method SparsePCGC.
Multi-vehicle experiment platform: A Digital Twin Realization Method
To confirm effectiveness of this method, a prototype system is developed, which consists of sand table testbed, its twin system and cloud.
CATRO: Channel Pruning via Class-Aware Trace Ratio Optimization
Deep convolutional neural networks are shown to be overkill with high parametric and computational redundancy in many application scenarios, and an increasing number of works have explored model pruning to obtain lightweight and efficient networks.
Multi-lane Unsignalized Intersection Cooperation with Flexible Lane Direction based on Multi-vehicle Formation Control
Unsignalized intersection cooperation of connected and automated vehicles (CAVs) is able to eliminate green time loss of signalized intersections and improve traffic efficiency.
Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking
Early tracking-by-detection algorithms need to do two feature extractions for detection and tracking.
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict Resolution
Multi-vehicle coordinated decision making and control can improve traffic efficiency while guaranteeing driving safety.
Multiscale Point Cloud Geometry Compression
Recent years have witnessed the growth of point cloud based applications because of its realistic and fine-grained representation of 3D objects and scenes.
Learned Point Cloud Geometry Compression
This paper presents a novel end-to-end Learned Point Cloud Geometry Compression (a. k. a., Learned-PCGC) framework, to efficiently compress the point cloud geometry (PCG) using deep neural networks (DNN) based variational autoencoders (VAE).
Combining Deep Reinforcement Learning and Safety Based Control for Autonomous Driving
Combining deep reinforcement learning and safety based control can get good performance for self-driving and collision avoidance.
Robotics
