Adversarial Attack on Trajectory Prediction for Autonomous Vehicles with Generative Adversarial Networks

Accurate trajectory prediction is crucial for autonomous vehicles to realize safe driving. Current trajectory prediction approaches generally rely on deep neural networks, which are susceptible to adversarial attacks. To evaluate the adversarial robustness and security of deep-learning-based trajectory prediction models, this paper proposes an adversarial attack method on trajectory prediction using generative adversarial networks (GANs). First, a novel LSTM-based attack trajectory model named Adv-GAN is proposed considering both the temporal and spatial driving features. The networks in Adv-GAN are trained through game learning between the generator and the discriminator to obtain the adversarial trajectories with real driving feature distribution. Furthermore, the generated trajectory is optimized with the vehicle kinematics model for driving feasibility on roads. The derived adversarial attack can lead to considerable deviations in trajectory prediction which affects driving safety for autonomous vehicles. We evaluate the proposed Adv-GAN on three public datasets, and experimental results show the effectiveness with better attack performance compared to a state-of-the-art adversarial attack model.