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Autonomous-Driving-Testing-Survey

Review

A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation.
Z Zhong, Y Tang, Y Zhou, VO Neves, Y Liu, et al.
ArXiv, 2021.

An investigation into the state-of-the-practice autonomous driving testing.
G Lou, Y Deng, X Zheng, T Zhang, M Zhang.
ArXiv, 2021.

自动驾驶智能系统测试研究综述.
朱向雷, 王海弛, 尤翰墨, 张蔚珩, 张颖异, 刘爽, et al.
软件学报, 2021.

Metrics

Quality metrics and oracles for autonomous vehicles testing.
G Jahangirova, A Stocco, et al.
ICST, 2021.

AD Simulations

Lidarsim: Realistic lidar simulation by leveraging the real world.
S Manivasagam, S Wang, K Wong, et al.
CVPR, 2020.

AADS Augmented autonomous driving simulation.
W Li, CW Pan, R Zhang, JP Ren, YX Ma, et al.
Science Robotics, 2021.

DataSets

Advanced pedestrian dataset augmentation for autonomous driving.
A Vobecky, M Uricár, D Hurych, et al.
ICCV, 2019.

CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving.
K Li, K Chen, H Wang, L Hong, C Ye, J Han, et al.
ArXiv, 2022.

Test cases generations

Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems.
M Zhang, Y Zhang, L Zhang, C Liu, et al.
ASE, 2018.

Generating effective test cases for self-driving cars from police reports.
A Gambi, T Huynh, G Fraser.
ESEC, 2019.

Automatically testing self-driving cars with search-based procedural content generation.
A Gambi, M Mueller, G Fraser.
ISSTA, 2019.

Requirements-driven test generation for autonomous vehicles with machine learning components.
CE Tuncali, G Fainekos, D Prokhorov, et al.
TIV, 2020.

Surfelgan: Synthesizing realistic sensor data for autonomous driving.
Z Yang, Y Chai, D Anguelov, Y Zhou, et al.
CVPR, 2020.

Adversarial Test

Simple physical adversarial examples against end-to-end autonomous driving models.
A Boloor, X He, C Gill, Y Vorobeychik, et al.
ICESS, 2019.

Physically realizable adversarial examples for lidar object detection.
J Tu, M Ren, S Manivasagam, et al.
CVPR, 2020.

Deepbillboard: Systematic physical-world testing of autonomous driving systems.
H Zhou, W Li, Z Kong, J Guo, Y Zhang, et al.
ICSE, 2020.

Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.
S Feng, X Yan, H Sun, Y Feng, HX Liu.
Nature communications, 2021.

Can We Use Arbitrary Objects to Attack LiDAR Perception in Autonomous Driving?
Y Zhu, C Miao, T Zheng, F Hajiaghajani, L Su, et al.
CCS, 2021.

Exploring adversarial robustness of multi-sensor perception systems in self driving.
J Tu, H Li, X Yan, M Ren, Y Chen, M Liang, et al.
ArXiv, 2021.

Corner Case

Finding critical scenarios for automated driving systems: A systematic literature review.
X Zhang, J Tao, K Tan, M Törngren, et al.
ArXiv, 2021.

Black box test

Deeptest: Automated testing of deep-neural-network-driven autonomous cars.
Y Tian, K Pei, S Jana, B Ray, et al.
ICSE, 2018.

Deceiving image-to-image translation networks for autonomous driving with adversarial perturbations.
L Wang, W Cho, KJ Yoon.
RAL, 2020.

Testing deep learning based visual perception for automated driving.
S Abrecht, L Gauerhof, C Gladisch, K Groh, et al.
ACM Transactions on Cyber-Physical Systems, 2021.

Multi-model

Detecting Safety Problems of Multi-Sensor Fusion in Autonomous Driving.
Z Zhong, Z Hu, S Guo, X Zhang, Z Zhong, et al.
ArXiv, 2022.

White box test

Deepxplore: Automated whitebox testing of deep learning systems.
K Pei, Y Cao, J Yang, S Jana.
SOSP, 2017.

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