Autonomous Driving
1424 papers with code • 4 benchmarks • 66 datasets
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)
Benchmarks
These leaderboards are used to track progress in Autonomous Driving
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Libraries
Use these libraries to find Autonomous Driving models and implementationsDatasets
CARLA
Waymo Open Dataset
AirSim
Virtual KITTI
TORCS
IDD
INTERACTION Dataset
ApolloScape
SemanticPOSS
WoodScape
Latest papers with no code
BoostRad: Enhancing Object Detection by Boosting Radar Reflections
Subsequently, a second DNN is employed to detect objects within the boosted reflection image.
Multimodal Fusion on Low-quality Data: A Comprehensive Survey
Multimodal fusion focuses on integrating information from multiple modalities with the goal of more accurate prediction, which has achieved remarkable progress in a wide range of scenarios, including autonomous driving and medical diagnosis.
Cost-Sensitive Uncertainty-Based Failure Recognition for Object Detection
Therefore, a process that mitigates false detections is crucial for both safety and accuracy.
On the Road to Clarity: Exploring Explainable AI for World Models in a Driver Assistance System
Moreover, we propose a custom feature map visualization technique to analyze the internal convolutional layers in the VAE to explain internal causes of poor reconstruction that may lead to dangerous traffic scenarios in AD applications.
Integration of Mixture of Experts and Multimodal Generative AI in Internet of Vehicles: A Survey
Generative AI (GAI) can enhance the cognitive, reasoning, and planning capabilities of intelligent modules in the Internet of Vehicles (IoV) by synthesizing augmented datasets, completing sensor data, and making sequential decisions.
Learning Car-Following Behaviors Using Bayesian Matrix Normal Mixture Regression
Learning and understanding car-following (CF) behaviors are crucial for microscopic traffic simulation.
A Survey on Intermediate Fusion Methods for Collaborative Perception Categorized by Real World Challenges
This survey analyzes intermediate fusion methods in collaborative perception for autonomous driving, categorized by real-world challenges.
OccGen: Generative Multi-modal 3D Occupancy Prediction for Autonomous Driving
Existing solutions for 3D semantic occupancy prediction typically treat the task as a one-shot 3D voxel-wise segmentation perception problem.
LaneCorrect: Self-supervised Lane Detection
Lane detection has evolved highly functional autonomous driving system to understand driving scenes even under complex environments.
PointDifformer: Robust Point Cloud Registration With Neural Diffusion and Transformer
Point cloud registration is a fundamental technique in 3-D computer vision with applications in graphics, autonomous driving, and robotics.
