An Automated Analysis Framework for Trajectory Datasets

Glasmacher, Elke; Krajewski, Robert J.; Eckstein, Lutz

doi:10.48550/arxiv.2202.07438

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A Slicing Techniques (E1 I1)1

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2022

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“…Individual states are derived from the relative positional data of a trajectory, including speed, acceleration, and jerk. The interaction features utilize surrogate safety metrics such as time headway, time-to-collision, and deceleration rate to avoid a crash [51], [52].…”

Section: A Slicing Techniques (E1 I1)mentioning

confidence: 99%

On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles

Zhang

Sun

et al. 2022

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. This involves a detailed exploration of various approaches, including data slicing methods, perturbation techniques, model architecture changes, and post-training adjustments. In the literature, we see many promising methods for increasing robustness, which are necessary for safe and reliable autonomous driving.

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Section: A Slicing Techniques (E1 I1)mentioning

confidence: 99%