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 171–180 of 2605 papers

TitleStatusHype
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy TrafficCode1
Driving Style Representation in Convolutional Recurrent Neural Network Model of Driver IdentificationCode1
CMDFusion: Bidirectional Fusion Network with Cross-modality Knowledge Distillation for LIDAR Semantic SegmentationCode1
3DMOTFormer: Graph Transformer for Online 3D Multi-Object TrackingCode1
Efficient and Effective Generation of Test Cases for Pedestrian Detection -- Search-based Software Testing of Baidu Apollo in SVLCode1
Efficient Object Detection in Autonomous Driving using Spiking Neural Networks: Performance, Energy Consumption Analysis, and Insights into Open-set Object DiscoveryCode1
Efficient Reinforcement Learning for Autonomous Driving with Parameterized Skills and PriorsCode1
Energy-based Detection of Adverse Weather Effects in LiDAR DataCode1
Continual Driving Policy Optimization with Closed-Loop Individualized CurriculaCode1
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Benchmark Results

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