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 2026–2050 of 2605 papers

TitleStatusHype
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection—0
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression—0
PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting—0
Parallelized Interactive Machine Learning on Autonomous Vehicles—0
Partial Weight Adaptation for Robust DNN Inference—0
Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving—0
Passenger hazard perception based on EEG signals for highly automated driving vehicles—0
PatchContrast: Self-Supervised Pre-training for 3D Object Detection—0
Path Planning in Support of Smart Mobility Applications using Generative Adversarial Networks—0
PBP: Path-based Trajectory Prediction for Autonomous Driving—0
PC-DAN: Point Cloud based Deep Affinity Network for 3D Multi-Object Tracking (Accepted as an extended abstract in JRDB-ACT Workshop at CVPR21)—0
Pedestrian and Passenger Interaction with Autonomous Vehicles: Field Study in a Crosswalk Scenario—0
Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network—0
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints—0
Pedestrian Detection in Low-Light Conditions: A Comprehensive Survey—0
Pedestrian detection with high-resolution event camera—0
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors—0
Pedestrian Models for Autonomous Driving Part I: Low-Level Models, from Sensing to Tracking—0
Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior—0
Pedestrian Prediction by Planning using Deep Neural Networks—0
Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks—0
Peek into the Future Camera-based Occupant Sensing in Configurable Cabins for Autonomous Vehicles—0
Perception Entropy: A Metric for Multiple Sensors Configuration Evaluation and Design—0
Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles—0
Performance Analysis of RSU-based Multihomed Multilane Vehicular Networks—0
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Benchmark Results

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