2024年是自动驾驶功能集中爆发的一年，各类主流方案如BEV检测、在线地图、occupancy networks、时序模型都陆续上车，功能层面上越来越接近L3,，甚至L4级别的功能也陆续具备了。可以说，自动驾驶撑起了整个AI领域的半边天，技术之密集，实属罕见！
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（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