Self-Driving Cars
169 papers with code • 0 benchmarks • 15 datasets
Self-driving cars : the task of making a car that can drive itself without human guidance.
( Image credit: Learning a Driving Simulator )
Benchmarks
These leaderboards are used to track progress in Self-Driving Cars
Libraries
Use these libraries to find Self-Driving Cars models and implementations
DDAD
Lost and Found
Shifts
Talk2Car
BDD-X
Argoverse 2 Motion Forecasting
SOD
HPD
OC
ELAS
Most implemented papers
VisualBackProp: efficient visualization of CNNs
devansh20la/VisualBackprop
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We furthermore justify our approach with theoretical arguments and theoretically confirm that the proposed method identifies sets of input pixels, rather than individual pixels, that collaboratively contribute to the prediction.
3D Packing for Self-Supervised Monocular Depth Estimation
TRI-ML/packnet-sfm
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CVPR 2020
Although cameras are ubiquitous, robotic platforms typically rely on active sensors like LiDAR for direct 3D perception.
PointPainting: Sequential Fusion for 3D Object Detection
Song-Jingyu/PointPainting
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CVPR 2020
Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature.
Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data
StanfordASL/Trajectron-plus-plus
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ECCV 2020
Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation.
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack
realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition
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Via BASAR, we find on-manifold adversarial samples are extremely deceitful and rather common in skeletal motions, in contrast to the common belief that adversarial samples only exist off-manifold.
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
First, we introduce neuron coverage for systematically measuring the parts of a DL system exercised by test inputs.
On a Formal Model of Safe and Scalable Self-driving Cars
In the second part we describe a design of a system that adheres to our safety assurance requirements and is scalable to millions of cars.
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
This article presents the first comprehensive survey on adversarial attacks on deep learning in Computer Vision.
MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation
vita-epfl/monoloco
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ICCV 2019
We tackle the fundamentally ill-posed problem of 3D human localization from monocular RGB images.
Scaling Out-of-Distribution Detection for Real-World Settings
We conduct extensive experiments in these more realistic settings for out-of-distribution detection and find that a surprisingly simple detector based on the maximum logit outperforms prior methods in all the large-scale multi-class, multi-label, and segmentation tasks, establishing a simple new baseline for future work.
