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 5776–5800 of 6092 papers

TitleStatusHype
Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attentionCode0
X-DECODE: EXtreme Deblurring with Curriculum Optimization and Domain EqualizationCode0
Fast Scene Understanding for Autonomous DrivingCode0
Reinforcement Learning and Deep Learning based Lateral Control for Autonomous DrivingCode0
What's There in the DarkCode0
Make Interval Bound Propagation great againCode0
Uncertainty-Aware Learning from Demonstration using Mixture Density Networks with Sampling-Free Variance ModelingCode0
Fast Ensemble Learning Using Adversarially-Generated Restricted Boltzmann MachinesCode0
Snow Removal for LiDAR Point Clouds with Spatio-temporal Conditional Random FieldsCode0
Uncertainty-aware LiDAR Panoptic SegmentationCode0
Deep Recurrent Gaussian Process with Variational Sparse Spectrum ApproximationCode0
AutoCure: Automated Tabular Data Curation Technique for ML PipelinesCode0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Reinforcement Learning with a TerminatorCode0
Partial-Label Learning with a Reject OptionCode0
Failing to Learn: Autonomously Identifying Perception Failures for Self-driving CarsCode0
Vehicle Behavior Prediction by Episodic-Memory Implanted NDTCode0
YOLO-CCA: A Context-Based Approach for Traffic Sign DetectionCode0
Uncertainty-boosted Robust Video Activity AnticipationCode0
Reliable Disentanglement Multi-view Learning Against View Adversarial AttacksCode0
Uncertainty Calibration and its Application to Object DetectionCode0
Software and application patterns for explanation methodsCode0
Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between AgentsCode0
Exploring the Limitations of Behavior Cloning for Autonomous DrivingCode0
Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth EstimationCode0
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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