SOTAVerified

Autonomous Driving

Autonomous driving is the task of driving a vehicle 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: Exploring the Limitations of Behavior Cloning for Autonomous Driving)

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2976–3000 of 6092 papers

TitleStatusHype
HCRMP: A LLM-Hinted Contextual Reinforcement Learning Framework for Autonomous Driving—0
Large receptive field strategy and important feature extraction strategy in 3D object detection—0
Large-scale 3D point cloud representations via graph inception networks with applications to autonomous driving—0
Cross-Cluster Shifting for Efficient and Effective 3D Object Detection in Autonomous Driving—0
Large-Scale Bandwidth and Power Optimization for Multi-Modal Edge Intelligence Autonomous Driving—0
Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset—0
Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset—0
Ego-centric Learning of Communicative World Models for Autonomous Driving—0
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead—0
Depth Monocular Estimation with Attention-based Encoder-Decoder Network from Single Image—0
Large-scale, real-time visual-inertial localization revisited—0
AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models—0
Hardware Accelerators in Autonomous Driving—0
Hardware Acceleration of Lane Detection Algorithm: A GPU Versus FPGA Comparison—0
LaserFlow: Efficient and Probabilistic Object Detection and Motion Forecasting—0
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving—0
Rotating-star Pattern for Camera Calibration—0
CRLLK: Constrained Reinforcement Learning for Lane Keeping in Autonomous Driving—0
Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics—0
Latency optimized Deep Neural Networks (DNNs): An Artificial Intelligence approach at the Edge using Multiprocessor System on Chip (MPSoC)—0
HAMF: A Hybrid Attention-Mamba Framework for Joint Scene Context Understanding and Future Motion Representation Learning—0
LatentFormer: Multi-Agent Transformer-Based Interaction Modeling and Trajectory Prediction—0
Design of Automatic Driving Safety Level and Positioning Accuracy—0
Designs and Implementations in Neural Network-based Video Coding—0
CRKD: Enhanced Camera-Radar Object Detection with Cross-modality Knowledge Distillation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ReasonNetDriving Score79.95—Unverified
2InterFuserDriving Score76.18—Unverified
3TCPDriving Score75.14—Unverified
4TF++ WPDriving Score66.32—Unverified
5Learning From All Vehicles (LAV)Driving Score61.85—Unverified
6TransFuserDriving Score61.18—Unverified
7TransFuser (Reproduced)Driving Score55.04—Unverified
8TCP (Reproduced)Driving Score47.91—Unverified
9Latent TransFuserDriving Score45.2—Unverified
10GRIADDriving Score36.79—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC69.17—Unverified
2TransFuserRC56.36—Unverified
#ModelMetricClaimedVerifiedStatus
1Geometric FusionRC86.91—Unverified
2TransFuserRC78.41—Unverified