adversarial autonomous car
220265571
Towards Robust LiDAR based Perception in Autonomous Driving: General Black box Adversarial Sensor Attack and Countermeasures
Perception plays a pivotal role in autonomous driving systems, which utilizes onboard sensors like cameras and LiDARs (Light Detection and Ranging) to assess surroundings. Recent studies have demonstrated that LiDAR based perception is vulnerable to spoofing attacks, in which adversaries spoof a fake vehicle in front of a victim self driving car by strategically transmitting laser signals to the victim's LiDAR sensor. However, existing attacks suffer from effectiveness and generality limitations. In this work, we perform the first study to explore the general vulnerability of current LiDAR based perception architectures and discover that the ignored occlusion patterns in LiDAR point clouds make self driving cars vulnerable to spoofing attacks. We construct the first black box spoofing attack based on our identified vulnerability, which universally achieves around 80% mean success rates on all target models. We perform the first defense study, proposing CARLO to mitigate LiDAR spoofing attacks. CARLO detects spoofed data by treating ignored occlusion patterns as invariant physical features, which reduces the mean attack success rate to 5.5% Meanwhile, we take the first step towards exploring a general architecture for robust LiDAR based perception, and propose SVF that embeds the neglected physical features into end to end learning. SVF further reduces the mean attack success rate to around 2.3%
USENIX Security Symposium
Jiachen Sun Yulong Cao Qi Alfred Chen Z Morley Mao
60440441
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present. Research on GAN is rapidly growing and there are many variants of the original GAN focusing on various aspects of deep learning. GAN are perceived as the most impactful direction of machine learning in the last decade. This paper focuses on the application of GAN in autonomous driving including topics such as advanced data augmentation, loss function learning, semi supervised learning, etc. We formalize and review key applications of adversarial techniques and discuss challenges and open problems to be addressed.
ArXiv
Michal Uricar Pavel Krizek David Hurych Ibrahim Sobh Senthil Kumar Yogamani Patrick Denny
199658114
Deep Learning Tracking for Autonomous Flying Systems Under Adversarial Inputs
We propose a game theory based deep learning tracking control scheme to enable a holonomic flying system to perform an autonomous trajectory tracking task, when considering saturating actuators, adversarial inputs, and nonquadratic cost functionals. The problem is formulated as a two player zero sum game, whose online solution is computed by learning the saddle point strategies in real time. Three approximators, namely a critic and two actors, are tuned online using data generated in real time along the system trajectories. The adaptive control character of the algorithm requires a persistence of excitation condition to be a priori validated, which is relaxed by using. a deep learning approach, based on experience replay with multiple layers. A robustifying control term is added to eliminate the effect of residual errors, leading to asymptotic stability of the equilibrium point of the closed loop system. A simulation of a target tracking application, where the measurements available to the aerial system are perturbed by persistent adversaries, is performed to validate the effectiveness of the proposed approach.
IEEE Transactions on Aerospace and Electronic Systems
Luis Rodolfo Garcia Carrillo Kyriakos G Vamvoudakis
221572489
Modeling Car Following Behaviors and Driving Styles with Generative Adversarial Imitation Learning
Building a human like car following model that can accurately simulate drivers' car following behaviors is helpful to the development of driving assistance systems and autonomous driving. Recent studies have shown the advantages of applying reinforcement learning methods in car following modeling. However, a problem has remained where it is difficult to manually determine the reward function. This paper proposes a novel car following model based on generative adversarial imitation learning. The proposed model can learn the strategy from drivers' demonstrations without specifying the reward. Gated recurrent units was incorporated in the actor critic network to enable the model to use historical information. Drivers' car following data collected by a test vehicle equipped with a millimeter wave radar and controller area network acquisition card was used. The participants were divided into two driving styles by K means with time headway and time headway when braking used as input features. Adopting five fold cross validation for model evaluation, the results show that the proposed model can reproduce drivers' car following trajectories and driving styles more accurately than the intelligent driver model and the recurrent neural network based model, with the lowest average spacing error (19.40% and speed validation error (5.57% as well as the lowest Kullback Leibler divergences of the two indicators used for driving style clustering.
Sensors
Yang Zhou Rui Fu Chang Wang Ruibin Zhang
220315064
Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective
Autonomous vehicles (AVs) rely on accurate and robust sensor observations for safety critical decision making in a variety of conditions. The fundamental building blocks of such systems are sensors and classifiers that process ultrasound, radar, GPS, lidar, and camera signals [1] It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations and can even be adversarial perturbations (APs)
IEEE Signal Processing Magazine
Apostolos Modas Ricardo Sanchez-Matilla Pascal Frossard Andrea Cavallaro
224705298
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing
There is considerable evidence that deep neural networks are vulnerable to adversarial perturbations applied directly to their digital inputs. However, it remains an open question whether this translates to vulnerabilities in real world systems. Specifically, in the context of image inputs to autonomous driving systems, an attack can be achieved only by modifying the physical environment, so as to ensure that the resulting stream of video inputs to the car's controller leads to incorrect driving decisions. Inducing this effect on the video inputs indirectly through the environment requires accounting for system dynamics and tracking viewpoint changes. We propose a scalable and efficient approach for finding adversarial physical modifications, using a differentiable approximation for the mapping from environmental modifications namely, rectangles drawn on the road to the corresponding video inputs to the controller network. Given the color, location, position, and orientation parameters of the rectangles, our mapping composites them onto pre recorded video streams of the original environment. Our mapping accounts for geometric and color variations, is differentiable with respect to rectangle parameters, and uses multiple original video streams obtained by varying the driving trajectory. When combined with a neural network based controller, our approach allows the design of adversarial modifications through end to end gradient based optimization. We evaluate our approach using the Carla autonomous driving simulator, and show that it is significantly more scalable and far more effective at generating attacks than a prior black box approach based on Bayesian Optimization.
ArXiv
Jinghan Yang Adith Boloor Ayan Chakrabarti Xuan Zhang Yevgeniy Vorobeychik
233225920
Vehicle Trajectory Prediction based on Social Generative Adversarial Network for Self Driving Car Applications
Self driving or autonomous vehicles need to efficiently and continuously navigate in complex traffic environments by analyzing the surrounding scene, understanding the behavior of other traffic agents, and predicting their future trajectories. The main goal is to plan a safe motion and reduce the reaction time for possibly imminent hazards. A critical and challenging problem considered in this paper is to explore the movement patterns of surrounding traffic agents and accurately predict their future trajectories for helping the vehicle make reasonable decision. To solve the problem, a deep learning based framework is proposed in this paper for predicting trajectories of autonomous vehicles. The key is to train a social GAN (generative adversarial network) deep model for prediction of vehicle trajectory. The presented experimental results have verified that the proposed social GAN based approach outperforms the traditional Social LSTM (long short term memory) based method.
2020 International Symposium on Computer, Consumer and Control (IS3C)
Li-Wei Kang Chih-Chung Hsu I-Shan Wang Ting-Lei Liu Shih-Yu Chen Chuan-Yu Chang
221088217
Multi Adversarial Safety Analysis for Autonomous Vehicles
This work in progress considers reachability based safety analysis in the domain of autonomous driving in multi agent systems. We formulate the safety problem for a car following scenario as a differential game and study how different modelling strategies yield very different behaviors regardless of the validity of the strategies in other scenarios. Given the nature of real life driving scenarios, we propose a modeling strategy in our formulation that accounts for subtle interactions between agents, and compare its Hamiltonian results to other baselines. Our formulation encourages reduction of conservativeness in Hamilton Jacobi safety analysis to provide better safety guarantees during navigation.
Gilbert Bahati Marsalis Gibson Alexandre M Bayen
211051101
Legislating Autonomous Vehicles against the Backdrop of Adversarial Machine Learning Findings
Recent studies on adversarial machine learning1 made Michael Grossman, a Texas based injury lawyer, skeptical of the viability of autonomous vehicles.2 These studies had pointed out that adversarial attacks or perturbations on pictures makes it difficult for the algorithm to correctly classify the content of that picture. If this is applied to traffic sign recognition, simple graffiti on the sign could mislead the algorithm that is analyzing the picture of the traffic sign captured by the camera. 3 Rather than recognizing the traffic sign for what it is, the algorithm could attribute a different meaning to the traffic sign. The consequences could be disastrous, especially if, for example, a stop sign would be read as a speeding sign.4 When rational car manufacturers know this defect, they will not proceed with the marketing of autonomous vehicles.
ICCVE
Steven Van Uytsel
210930228
Legislating Autonomous Vehicles against the Backdrop of Adversarial Machine Learning Findings
Recent studies on adversarial machine learning1 made Michael Grossman, a Texas based injury lawyer, skeptical of the viability of autonomous vehicles.2 These studies had pointed out that adversarial attacks or perturbations on pictures makes it difficult for the algorithm to correctly classify the content of that picture. If this is applied to traffic sign recognition, simple graffiti on the sign could mislead the algorithm that is analyzing the picture of the traffic sign captured by the camera. 3 Rather than recognizing the traffic sign for what it is, the algorithm could attribute a different meaning to the traffic sign. The consequences could be disastrous, especially if, for example, a stop sign would be read as a speeding sign.4 When rational car manufacturers know this defect, they will not proceed with the marketing of autonomous vehicles.
2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE)
Steven Van Uytsel