SOTAVerified

Autonomous Vehicles

Autonomous vehicles is the task of making a vehicle that can guide itself 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: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1476–1500 of 2605 papers

TitleStatusHype
Audio-Visual Self-Supervised Terrain Type Discovery for Mobile Platforms—0
Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks—0
Augmenting End-to-End Steering Angle Prediction with CAN Bus Data—0
Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles—0
A Unified Toll Lane Framework for Autonomous and High-Occupancy Vehicles in Interactive Mixed Autonomy—0
AUTHENTICATION: Identifying Rare Failure Modes in Autonomous Vehicle Perception Systems using Adversarially Guided Diffusion Models—0
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving—0
aUToLights: A Robust Multi-Camera Traffic Light Detection and Tracking System—0
Automated Data Curation Using GPS & NLP to Generate Instruction-Action Pairs for Autonomous Vehicle Vision-Language Navigation Datasets—0
Automated design of error-resilient and hardware-efficient deep neural networks—0
Automated Detecting and Placing Road Objects from Street-level Images—0
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving—0
Automated Generation of Diverse and Challenging Scenarios for Test and Evaluation of Autonomous Vehicles—0
Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology—0
Automatically Learning Fallback Strategies with Model-Free Reinforcement Learning in Safety-Critical Driving Scenarios—0
Automatic Calibration of Multiple 3D LiDARs in Urban Environments—0
Automatic driving lane change safety prediction model based on LSTM—0
Automatic Testing With Reusable Adversarial Agents—0
Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint—0
Automatic Vehicle Detection using DETR: A Transformer-Based Approach for Navigating Treacherous Roads—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Autonomous Braking and Throttle System: A Deep Reinforcement Learning Approach for Naturalistic Driving—0
Autonomous Cars: Vision based Steering Wheel Angle Estimation—0
Autonomous Driving and Residential Location Preferences: Evidence from a Stated Choice Survey—0
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1BAAMA3DP22.85—Unverified
2GSNetA3DP20.21—Unverified