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
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.
Editable Neural Networks
We empirically demonstrate the effectiveness of this method on large-scale image classification and machine translation tasks.
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.
