Deep Learning Based Classification of Pedestrian Vulnerability Trained on Synthetic Datasets The reliable detection of vulnerable road users and the assessment of the actual vulnerability is an important task for the collision warning algorithms of driver assistance systems. Current systems make assumptions about the road geometry which can lead to misclassification. We propose a deep learningbased approach to reliably detect pedestrians and classify their vulnerability based on the traffic area they are walking in. Since there are no prelabeled datasets available for this task, we developed a method to train a network first on custom synthetic data and then use the network to augment a customerprovided training dataset for a neural network working on real world images. The evaluation shows that our network is able to accurately classify the vulnerability of pedestrians in complex real world scenarios without making assumptions on road geometry.

Adaptive Adversarial Videos on Roadside Billboards: Dynamically Modifying Trajectories of Autonomous Vehicles Deep neural networks (DNNs) are being incorporated into various autonomous systems like selfdriving cars and robots. However, there is a rising concern about the robustness of these systems because of their susceptibility to adversarial attacks on DNNs. Past research has established that DNNs used for classification and object detection are prone to attacks causing targeted misclassification. In this paper, we show the effectiveness of an adversarial dynamic attack on an endtoend trained DNN controlling an autonomous vehicle. We launch the attack by installing a billboard on the roadside and displaying videos to approaching vehicles to cause the DNN controller in the vehicle to generate steering commands that cause, for example, unintended lane changes or motion off the road causing accidents. The billboard has an integrated camera estimating the pose of the oncoming vehicle. The approach enables dynamic adversarial perturbation that adapts to the relative pose of the vehicle and uses the dynamics of the vehicle to steer it along adversarychosen trajectories while being robust to variations in view, lighting, and weather. We demonstrate the effectiveness of the attack on a recently published offtheshelf endtoend learningbased autonomous navigation system in a highfidelity simulator, CARLA (CAR Learning to Act). The proposed approach may also be applied to other systems driven by an endtoend trained network.