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 1351–1400 of 2605 papers

TitleStatusHype
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
Hypercongestion, autonomous vehicle, and urban spatial structure—0
Personalization and Recommendation Technologies for MaaS—0
Personalized and Context-aware Route Planning for Edge-assisted Vehicles—0
Personalized Federated Learning for Cross-view Geo-localization—0
Personalized Federated Learning of Driver Prediction Models for Autonomous Driving—0
Physical Adversarial Examples for Multi-Camera Systems—0
Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis—0
Physical Computing for Materials Acceleration Platforms—0
Physically constrained short-term vehicle trajectory forecasting with naive semantic maps—0
Physically Interpretable Probabilistic Domain Characterization—0
Physics-Informed Adaptive Deep Koopman Operator Modeling for Autonomous Vehicle Dynamics—0
*: Improving the 3D detector by introducing Voxel2Pillar feature encoding and extracting multi-scale features—0
PIP-Net: Pedestrian Intention Prediction in the Wild—0
Pixel-Level GPS Localization and Denoising using Computer Vision and 6G Communication Beams—0
Place recognition survey: An update on deep learning approaches—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Planning Reliability Assurance Tests for Autonomous Vehicles—0
Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models—0
Platoon--assisted Vehicular Cloud in VANET: Vision and Challenges—0
Plausibility Verification For 3D Object Detectors Using Energy-Based Optimization—0
P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors—0
Point Cloud Compression for Efficient Data Broadcasting: A Performance Comparison—0
Point Cloud in the Air—0
Point Cloud Segmentation Using Sparse Temporal Local Attention—0
PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM—0
PointNet on FPGA for Real-Time LiDAR Point Cloud Processing—0
Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction—0
Polar Collision Grids: Effective Interaction Modelling for Pedestrian Trajectory Prediction in Shared Space Using Collision Checks—0
Polling-systems-based Autonomous Vehicle Coordination in Traffic Intersections with No Traffic Signals—0
Poly-Autoregressive Prediction for Modeling Interactions—0
Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models—0
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know—0
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
Potential-based Credit Assignment for Cooperative RL-based Testing of Autonomous Vehicles—0
Potential Game-Based Decision-Making for Autonomous Driving—0
Powertrain Hybridization for Autonomous Vehicles—0
Practical Auto-Calibration for Spatial Scene-Understanding from Crowdsourced Dashcamera Videos—0
Taxonomy of Machine Learning Safety: A Survey and Primer—0
Practical Solutions for Machine Learning Safety in Autonomous Vehicles—0
Precise Ranging: Modeling Bias and Variance of Double-Sided Two-Way Ranging with TDoA Extraction under Multipath and NLOS Effects—0
Predicting Autonomous Vehicle Collision Injury Severity Levels for Ethical Decision Making and Path Planning—0
Predicting Future Lane Changes of Other Highway Vehicles using RNN-based Deep Models—0
Predicting Scene Parsing and Motion Dynamics in the Future—0
Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning—0
Predicting Vehicle Behaviors Over An Extended Horizon Using Behavior Interaction Network—0
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Benchmark Results

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