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 1501–1525 of 2605 papers

TitleStatusHype
"Is not the truth the truth?": Analyzing the Impact of User Validations for Bus In/Out Detection in Smartphone-based Surveys—0
EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception—0
A Tutorial on Adversarial Learning Attacks and Countermeasures—0
Flow-level Coordination of Connected and Autonomous Vehicles in Multilane Freeway Ramp Merging Areas—0
Merging Control Strategies of Connected and Autonomous Vehicles at Freeway On-Ramps: A Comprehensive Review—0
An Active and Contrastive Learning Framework for Fine-Grained Off-Road Semantic Segmentation—0
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset—0
Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving ScenariosCode1
Transferable and Adaptable Driving Behavior Prediction—0
Intelligent Autonomous Intersection Management—0
CRAT-Pred: Vehicle Trajectory Prediction with Crystal Graph Convolutional Neural Networks and Multi-Head Self-AttentionCode1
Real-time decision-making for autonomous vehicles under faults—0
Indy Autonomous Challenge -- Autonomous Race Cars at the Handling Limits—0
Causal Scene BERT: Improving object detection by searching for challenging groups of data—0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Two-Dimensional Arbitrary Angle of Arrival in Radar Target Simulation—0
Learning Interpretable, High-Performing Policies for Autonomous DrivingCode1
ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic MaskingCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
Multi-Agent Trajectory Prediction With Heterogeneous Edge-Enhanced Graph Attention NetworkCode1
Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison—0
CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement LearningCode1
5G enabled Mobile Edge Computing security for Autonomous Vehicles—0
TPC: Transformation-Specific Smoothing for Point Cloud ModelsCode0
Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic EnvironmentsCode1
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Benchmark Results

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