SOTAVerified

Autonomous Driving

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.

(Image credit: Exploring the Limitations of Behavior Cloning for Autonomous Driving)

Papers

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Showing 2601–2650 of 6092 papers

TitleStatusHype
Uncertainty-Aware Decision Transformer for Stochastic Driving Environments—0
Autonomous Driving using Spiking Neural Networks on Dynamic Vision Sensor Data: A Case Study of Traffic Light Change DetectionCode1
Uncertainty Quantification via Neural Posterior Principal Components—0
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies—0
The Robust Semantic Segmentation UNCV2023 Challenge Results—0
AutoEncoding Tree for City Generation and Applications—0
HPL-ViT: A Unified Perception Framework for Heterogeneous Parallel LiDARs in V2V—0
3D Multiple Object Tracking on Autonomous Driving: A Literature Review—0
InfraParis: A multi-modal and multi-task autonomous driving datasetCode0
V2X-Lead: LiDAR-based End-to-End Autonomous Driving with Vehicle-to-Everything Communication Integration—0
CLRmatchNet: Enhancing Curved Lane Detection with Deep Matching ProcessCode1
Addressing Data Misalignment in Image-LiDAR Fusion on Point Cloud Segmentation—0
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch—0
Probabilistic 3D Multi-Object Cooperative Tracking for Autonomous Driving via Differentiable Multi-Sensor Kalman FilterCode1
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge DistillationCode1
Benchmarking Local Robustness of High-Accuracy Binary Neural Networks for Enhanced Traffic Sign RecognitionCode1
Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Driving—0
Stackelberg Driver Model for Continual Policy Improvement in Scenario-Based Closed-Loop Autonomous DrivingCode1
Continual Driving Policy Optimization with Closed-Loop Individualized CurriculaCode1
Adversarial Attacks on Video Object Segmentation with Hard Region Discovery—0
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills—0
Distribution-Aware Continual Test-Time Adaptation for Semantic SegmentationCode1
Deep Learning-Based Connector Detection for Robotized Assembly of Automotive Wire Harnesses—0
Overview of Computer Vision Techniques in Robotized Wire Harness Assembly: Current State and Future Opportunities—0
FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous DrivingCode1
Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased Efficiency—0
Sequential Action-Induced Invariant Representation for Reinforcement LearningCode0
Pixel-wise Smoothing for Certified Robustness against Camera Motion PerturbationsCode0
Intent-Aware Autonomous Driving: A Case Study on Highway Merging Scenarios—0
Distributional Pareto-Optimal Multi-Objective Reinforcement LearningCode1
MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth CluesCode1
Electric Autonomous Mobility-on-Demand: Jointly Optimal Vehicle Design and Fleet Operation—0
Learning to Drive Anywhere—0
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video SegmentationCode1
FGFusion: Fine-Grained Lidar-Camera Fusion for 3D Object DetectionCode1
You can have your ensemble and run it too -- Deep Ensembles Spread Over Time—0
PPD: A New Valet Parking Pedestrian Fisheye Dataset for Autonomous Driving—0
LineMarkNet: Line Landmark Detection for Valet Parking—0
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles—0
Rethinking Imitation-based Planner for Autonomous DrivingCode2
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations—0
Safe POMDP Online Planning via Shielding—0
Pre-training on Synthetic Driving Data for Trajectory PredictionCode0
AR-TTA: A Simple Method for Real-World Continual Test-Time AdaptationCode0
Specification-Driven Video Search via Foundation Models and Formal Verification—0
DriveDreamer: Towards Real-world-driven World Models for Autonomous DrivingCode2
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering SupervisionCode2
Guided Online Distillation: Promoting Safe Reinforcement Learning by Offline Demonstration—0
Privileged to Predicted: Towards Sensorimotor Reinforcement Learning for Urban Driving—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95—Unverified
2InterFuserDriving Score76.18—Unverified
3TCPDriving Score75.14—Unverified
4TF++ WPDriving Score66.32—Unverified
5Learning From All Vehicles (LAV)Driving Score61.85—Unverified
6TransFuserDriving Score61.18—Unverified
7TransFuser (Reproduced)Driving Score55.04—Unverified
8TCP (Reproduced)Driving Score47.91—Unverified
9Latent TransFuserDriving Score45.2—Unverified
10GRIADDriving Score36.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17—Unverified
2TransFuserRC56.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91—Unverified
2TransFuserRC78.41—Unverified