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 2501–2525 of 2605 papers

TitleStatusHype
Safe Driving Capacity of Autonomous Vehicles—0
Multi-Modal Trajectory Prediction of Surrounding Vehicles with Maneuver based LSTMs—0
Spatial Uncertainty Sampling for End-to-End Control—0
A Tempt to Unify Heterogeneous Driving Databases using Traffic Primitives—0
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systems—0
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression—0
An Accelerated Approach to Safely and Efficiently Test Pre-Production Autonomous Vehicles on Public Streets—0
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems—0
A Bird's-eye View of Language Processing Projects at the Romanian Academy—0
Formal Security Analysis of Neural Networks using Symbolic IntervalsCode0
Boosting LiDAR-based Semantic Labeling by Cross-Modal Training Data Generation—0
Path Planning in Support of Smart Mobility Applications using Generative Adversarial Networks—0
HandyNet: A One-stop Solution to Detect, Segment, Localize & Analyze Driver HandsCode0
Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning ComponentsCode0
Quantifying the Economic Case for Electric Semi-Trucks—0
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object DetectorCode0
Road Segmentation Using CNN with GRU—0
Probabilistic Prediction of Vehicle Semantic Intention and Motion—0
Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields—0
Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles—0
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network ApproachCode0
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object ClassificationCode0
MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems—0
Localization under Topological Uncertainty for Lane Identification of Autonomous Vehicles—0
Multi-Agent Imitation Learning for Driving SimulationCode0
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Benchmark Results

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