其他
自动驾驶全栈学习路线正式推出了！
为了方便大家入门学习，自动驾驶之心为大家推出了近13个感知定位融合与标定学习路线，里面的论文和学习资料特别适合刚入门和转行的同学，内容较多，建议大家收藏后反复观看。
添加小助理微信AIDriver004，领取所有学习资料，备注：自动驾驶之心资料领取哦~
（一）3D目标检测系列
3D Object Detection for Autonomous Driving：A Review and New Outlooks 3D Object Detection from Images for Autonomous Driving A Survey A Survey of Robust LiDAR-based 3D Object Detection Methods for autonomous driving A Survey on 3D Object Detection Methods for Autonomous Driving Applications Deep Learning for 3D Point Cloud Understanding：A Survey Multi-Modal 3D Object Detection in Autonomous Driving：a survey Survey and Systematization of 3D Object Detection Models and Methods
（二）BEV感知综述
Delving into the Devils of Bird’s-eye-view Perception-A Review, Evaluation and Recipe Surround-View Vision-based 3D Detection for Autonomous Driving：A Survey Vision-Centric BEV Perception：A Survey Vision-RADAR fusion for Robotics BEV Detections：A Survey
（三）传感器标定综述
涉及多相机标定、毫米波与激光雷达标定、相机-激光雷达-毫米波雷达标定、相机-IMU标定、相机标定、鱼眼相机标定、在线标定等；
（四）Occupancy占用网络综述
Grid-Centric Traffic Scenario Perception for Autonomous Driving：A Comprehensive Review
（五）多模态融合感知综述
Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving：Datasets, Methods, and Challenges MmWave Radar and Vision Fusion for Object Detection in Autonomous Driving：A Review Multi-Modal 3D Object Detection in Autonomous Driving：A Survey Multi-modal Sensor Fusion for Auto Driving Perception：A Survey Multi-Sensor 3D Object Box Refinement for Autonomous Driving Multi-View Fusion of Sensor Data for Improved Perception and Prediction in Autonomous Driving
（六）端到端自动驾驶综述
End-to-end Autonomous Driving-Challenges and Frontiers Recent Advancements in End-to-End Autonomous Driving using Deep Learning
（七）自动驾驶规划控制综述
A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles Mobile Robot Path Planning in Dynamic Environments：A Survey Motion Planning and Control for Mobile Robot Navigation Using Machine Learning：A Survey Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles
（八）CUDA与C++加速
Cuda by Example CUDA for Engineers. An Introduction to High-Performance Parallel Computing-Addison Wesley GPU parallel program development using CUDA-CRC Press
（九）大模型与自动驾驶
Planning-oriented Autonomous Driving MINIGPT-4: ENHANCING VISION-LANGUAGE UNDERSTANDING WITH ADVANCED LARGE LANGUAGE MODELS LANGUAGEMPC: LARGE LANGUAGE MODELS AS DECISION MAKERS FOR AUTONOMOUS DRIVING HiLM-D: Towards High-Resolution Understanding in Multimodal Large Language Models for Autonomous Driving Driving with LLMs: Fusing Object-Level Vector Modality for Explainable Autonomous Driving DRIVEGPT4: INTERPRETABLE END-TO-END AUTONOMOUS DRIVING VIA LARGE LANGUAGE MODEL Drive Like a Human: Rethinking Autonomous Driving with Large Language Models Learning Transferable Visual Models From Natural Language Supervision BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation BEVGPT: Generative Pre-trained Large Model for Autonomous Driving Prediction, Decision-Making, and Planning
（十）轨迹预测与自动驾驶
Survey：Machine Learning for Autonomous Vehicle's Trajectory Prediction Situation Assessment of an Autonomous Emergency Vehicle Trajectory Prediction by Integrating Physics and Maneuver-Based Approaches Using Interactive Multiple Models A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models Vehicle Trajectory Prediction Considering Driver Uncertainty and Vehicle Dynamics Based on Dynamic Bayesian Network Naturalistic Driver Intention and Path Prediction Using Recurrent Neural Networks Intention-Aware Vehicle Trajectory Prediction Based on Spatial-Temporal Dynamic Attention Network for Internet of Vehicles Trajectory Prediction for Autonomous Driving Using Spatial-Temporal Graph Attention Transformer Multi-Vehicle_Collaborative_Learning_for_Trajectory_Prediction_With_Spatio-Temporal_Tensor_Fusion STAG A novel interaction-aware path prediction method based on Spatio-Temporal Attention Graphs for connected automated vehicles TNT Target-driveN Trajectory Prediction DenseTNT End-to-end Trajectory Prediction from Dense Goal Sets
（十一）在线高精地图
（十二）世界模型与自动驾驶
ADriver-I: A General World Model for Autonomous Driving DriveDreamer: Towards Real-world-driven World Models for Autonomous Driving Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving FIERY: Future Instance Prediction in Bird’s-Eye View from Surround Monocular Cameras GAIA-1: A Generative World Model for Autonomous Driving Model-Based Imitation Learning for Urban Driving OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving MUVO: A Multimodal Generative World Model for Autonomous Driving with Geometric Representations SEM2: Enhance Sample Efficiency and Robustness of End-to-end Urban Autonomous Driving via Semantic Masked World Model DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION MASTERING ATARI WITH DISCRETE WORLD MODELS LEARNING UNSUPERVISED WORLD MODELS FOR AUTONOMOUS DRIVING VIA DISCRETE DIFFUSION
(十三) NeRF与自动驾驶
3D Gaussian Splatting for Real-Time Radiance Field Rendering Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM F2-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories Instant Neural Graphics Primitives with a Multiresolution Hash Encoding MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures Neuralangelo: High-Fidelity Neural Surface Reconstruction UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering UniSim: A Neural Closed-Loop Sensor Simulator