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 1626–1650 of 6092 papers

TitleStatusHype
Optimal Trajectory Planning with Collision Avoidance for Autonomous Vehicle Maneuvering—0
Towards Adaptive Meta-Gradient Adversarial Examples for Visual TrackingCode0
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning—0
Object detection in adverse weather conditions for autonomous vehicles using Instruct Pix2Pix—0
Ranking-aware Continual Learning for LiDAR Place Recognition—0
Topology-Guided Knowledge Distillation for Efficient Point Cloud ProcessingCode0
Boosting Cross-spectral Unsupervised Domain Adaptation for Thermal Semantic Segmentation—0
Beyond Patterns: Harnessing Causal Logic for Autonomous Driving Trajectory Prediction—0
Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving—0
AI-CDA4All: Democratizing Cooperative Autonomous Driving for All Drivers via Affordable Dash-cam Hardware and Open-source AI Software—0
TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility—0
RESAR-BEV: An Explainable Progressive Residual Autoregressive Approach for Camera-Radar Fusion in BEV Segmentation—0
Priority-Driven Safe Model Predictive Control Approach to Autonomous Driving Applications—0
Natural Reflection Backdoor Attack on Vision Language Model for Autonomous Driving—0
What Do People Want to Know About Artificial Intelligence (AI)? The Importance of Answering End-User Questions to Explain Autonomous Vehicle (AV) DecisionsCode0
Multi-Objective Reinforcement Learning for Adaptive Personalized Autonomous Driving—0
PADriver: Towards Personalized Autonomous Driving—0
X-Driver: Explainable Autonomous Driving with Vision-Language Models—0
Closing the Loop: Motion Prediction Models beyond Open-Loop BenchmarksCode0
Predicting Road Surface Anomalies by Visual Tracking of a Preceding Vehicle—0
Reliable Disentanglement Multi-view Learning Against View Adversarial AttacksCode0
CaRaFFusion: Improving 2D Semantic Segmentation with Camera-Radar Point Cloud Fusion and Zero-Shot Image Inpainting—0
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation—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