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

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Showing 2501–2525 of 2605 papers

TitleStatusHype
Robust Glare Detection: Review, Analysis, and Dataset ReleaseCode0
The Impact of Partial Occlusion on Pedestrian DetectabilityCode0
ARC: Adversarially Robust Control Policies for Autonomous VehiclesCode0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural NetworksCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
Benchmarking 6DOF Outdoor Visual Localization in Changing ConditionsCode0
Performance Evaluation of Real-Time Object Detection for Electric ScootersCode0
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During DeploymentCode0
RobustMat: Neural Diffusion for Street Landmark Patch Matching under Challenging EnvironmentsCode0
Intention Recognition of Pedestrians and Cyclists by 2D Pose EstimationCode0
Detecting Adversarial Attacks on Neural Network Policies with Visual ForesightCode0
Traffic Signs in the Wild: Highlights from the IEEE Video and Image Processing Cup 2017 Student Competition [SP Competitions]Code0
Robust Sensor Fusion Algorithms Against Voice Command Attacks in Autonomous VehiclesCode0
DepthNet: Real-Time LiDAR Point Cloud Depth Completion for Autonomous VehiclesCode0
In-Place Zero-Space Memory Protection for CNNCode0
Fast Deep Stereo with 2D Convolutional Processing of Cost SignaturesCode0
CNN-based Lidar Point Cloud De-Noising in Adverse WeatherCode0
Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and LogosCode0
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving CarsCode0
A Safe Preference Learning Approach for Personalization with Applications to Autonomous VehiclesCode0
The Reasonable Crowd: Towards evidence-based and interpretable models of driving behaviorCode0
MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement LearningCode0
Depth- and Semantics-aware Multi-modal Domain Translation: Generating 3D Panoramic Color Images from LiDAR Point CloudsCode0
RRPN: Radar Region Proposal Network for Object Detection in Autonomous VehiclesCode0
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Benchmark Results

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