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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2076–2100 of 6092 papers

TitleStatusHype
DiVE: DiT-based Video Generation with Enhanced Control—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving—0
Distribution-aware Goal Prediction and Conformant Model-based Planning for Safe Autonomous Driving—0
A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Distributional Soft Actor-Critic with Harmonic Gradient for Safe and Efficient Autonomous Driving in Multi-lane Scenarios—0
Enabling 3D Object Detection with a Low-Resolution LiDAR—0
FSNet: Redesign Self-Supervised MonoDepth for Full-Scale Depth Prediction for Autonomous Driving—0
Distributionally Robust Trajectory Optimization Under Uncertain Dynamics via Relative Entropy Trust-Regions—0
Distributed Radiance Fields for Edge Video Compression and Metaverse Integration in Autonomous Driving—0
Distributed Learning for Vehicular Dynamic Spectrum Access in Autonomous Driving—0
Distributed Learning and Inference with Compressed Images—0
A Methodology to Identify Cognition Gaps in Visual Recognition Applications Based on Convolutional Neural Networks—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Distributed Coordination for Multi-Vehicle Systems in the Presence of Misbehaving Vehicles—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distilling Multi-modal Large Language Models for Autonomous Driving—0
A Methodological Review of Visual Road Recognition Procedures for Autonomous Driving Applications—0
3D StreetUnveiler with Semantic-Aware 2DGS—0
Distilling Monocular Foundation Model for Fine-grained Depth Completion—0
Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Model—0
Behavioural gap assessment of human-vehicle interaction in real and virtual reality-based scenarios in autonomous driving—0
Distance-Normalized Unified Representation for Monocular 3D Object Detection—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