zqzqz/AdvTrajectoryPrediction
Implementation of CVPR 2022 paper "On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles" https://arxiv.org/abs/2201.05057
This project helps automotive engineers and researchers understand how vulnerable autonomous vehicle trajectory prediction systems are to 'adversarial attacks.' It takes raw trajectory data from real-world driving datasets (like Apolloscape or NuScenes) and applies different attack methods to generate perturbed predictions. The output shows how much these attacks degrade the accuracy of predictions, helping developers build more robust self-driving car systems.
123 stars. No commits in the last 6 months.
Use this if you are developing or evaluating autonomous driving systems and need to assess the security and reliability of their trajectory prediction models against potential malicious interference.
Not ideal if you are looking for a general-purpose trajectory prediction model for deployment rather than a tool for adversarial robustness analysis.
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123
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22
Language
Python
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Last pushed
Jul 31, 2024
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