Papers with Code 아카이브

Autonomous Driving

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 6
평가표: 4
보존된 평가 기록: 23

Autonomous driving is the task of driving a vehicle without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

<span style="color:grey; opacity: 0.5">(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CEA-LIST/AD-RobustnessEval

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Evaluating Robustness over High Level Driving Instruction for Autonomous Driving

https://github.com/CHENGEZ/Attacking-by-Aligning

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Attacking by Aligning: Clean-Label Backdoor Attacks on Object Detection

https://github.com/CIRLMDP/CIRL

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Inverse Reinforcement Learning in Contextual MDPs

https://github.com/CMVS-Lab/SAC-Residual-Fusion-CARLA

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Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning

https://github.com/CN-TU/ids-backdoor

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Walling up Backdoors in Intrusion Detection Systems

https://github.com/CPFL/Autoware

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LMNet: Real-time Multiclass Object Detection on CPU using 3D LiDAR

https://github.com/CVLAB-Unibo/Learning2AdaptForStereo

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Learning to Adapt for Stereo

https://github.com/Cedarch/M3Net

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M3Net: Multimodal Multi-task Learning for 3D Detection, Segmentation, and Occupancy Prediction in Autonomous Driving

https://github.com/ChongjianGE/MetaBEV

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MetaBEV: Solving Sensor Failures for BEV Detection and Map Segmentation

https://github.com/CongliangLi/LabelImg3D

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Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision

https://github.com/CongliangLi/SMOPE-Net

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Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision

https://github.com/Coolshanlan/DDFH-active-3Ddet

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Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection

https://github.com/Daniel-Liu-c0deb0t/Adversarial-point-perturbations-on-3D-objects

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Adversarial shape perturbations on 3D point clouds

https://github.com/Daniel-xsy/RoboBEV

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Benchmarking and Improving Bird's Eye View Perception Robustness in Autonomous Driving

https://github.com/DanielCoelho112/priboot

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PRIBOOT: A New Data-Driven Expert for Improved Driving Simulations

https://github.com/Das-Boot/bot_or_not

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Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling

https://github.com/Dependable-Intelligent-Systems-Lab/xwhy/tree/main/examples/Point%20Cloud%20Examples

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Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations

https://github.com/Depth2World/VLADBench

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Fine-Grained Evaluation of Large Vision-Language Models in Autonomous Driving

https://github.com/DexterJZ/eval_driving_safety

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Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehicles

https://github.com/DingJianhao/stable-snn

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Robust Stable Spiking Neural Networks

https://github.com/Div99/W-Stereo-Disp

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Wasserstein Distances for Stereo Disparity Estimation

https://github.com/DriveVLA/OpenDriveVLA

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OpenDriveVLA: Towards End-to-end Autonomous Driving with Large Vision Language Action Model

https://github.com/E2E-AD/AD-MLP

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Rethinking the Open-Loop Evaluation of End-to-End Autonomous Driving in nuScenes

https://github.com/EGiunchiglia/ROAD-R

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ROAD-R: The Autonomous Driving Dataset with Logical Requirements

https://github.com/ENSTA-U2IS-AI/Multimodal_Deep_segmentation

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InfraParis: A multi-modal and multi-task autonomous driving dataset

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

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MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/EdAlexAguilar/reward_shaping

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Hierarchical Potential-based Reward Shaping from Task Specifications

https://github.com/EladSharony/miso

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Learning Multiple Initial Solutions to Optimization Problems

https://github.com/EnVision-Research/MotionDreamer

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Motion Dreamer: Realizing Physically Coherent Video Generation through Scene-Aware Motion Reasoning

https://github.com/Equinoxxxxx/MulCPred_code

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MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction

https://github.com/EricGuo5513/momask-codes

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MoMask: Generative Masked Modeling of 3D Human Motions

https://github.com/Erosinho13/FedDrive

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FedDrive: Generalizing Federated Learning to Semantic Segmentation in Autonomous Driving

https://github.com/EtienneGof/FunCLBM

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Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving Validation

https://github.com/EverettRichards/Edge-CAV

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Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception

https://github.com/Ezharjan/AutoSceneGen

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Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

https://github.com/FabianAmherd/CNN_ER_FA

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Heatmap-based Object Detection and Tracking with a Fully Convolutional Neural Network

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