Autonomous Driving
1445 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
Most implemented papers
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples.
Guiding Deep Learning System Testing using Surprise Adequacy
coinse/sadl
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Recently, a number of coverage criteria based on neuron activation values have been proposed.
GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Driving
xincoder/GRIP
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arXiv preprint 2020
Despite the advancement in the technology of autonomous driving cars, the safety of a self-driving car is still a challenging problem that has not been well studied.
SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
In this paper, we introduce SalsaNext for the uncertainty-aware semantic segmentation of a full 3D LiDAR point cloud in real-time.
CARRADA Dataset: Camera and Automotive Radar with Range-Angle-Doppler Annotations
In this work, we introduce CARRADA, a dataset of synchronized camera and radar recordings with range-angle-Doppler annotations.
YOLOP: You Only Look Once for Panoptic Driving Perception
hustvl/yolop
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A panoptic driving perception system is an essential part of autonomous driving.
EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
rwightman/pytorch-image-models
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Without performance loss on Cityscapes, our EfficientViT provides up to 13. 9$\times$ and 6. 2$\times$ GPU latency reduction over SegFormer and SegNeXt, respectively.
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Therefore, additional processing steps have to be performed in order to obtain pixel-accurate segmentation masks at the full image resolution.
Joint 3D Proposal Generation and Object Detection from View Aggregation
kujason/avod
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We present AVOD, an Aggregate View Object Detection network for autonomous driving scenarios.
The Earth ain't Flat: Monocular Reconstruction of Vehicles on Steep and Graded Roads from a Moving Camera
The proposed approach significantly improves the state-of-the-art for monocular object localization on arbitrarily-shaped roads.
