AGNS : Adversarial Attack against Human Trajectory Model Based on Attention-guidance and Node-selection
Bibliographic record
Abstract
As a crucial component of autonomous vehicles and intelligent robots, human trajectory prediction provides route planning for intelligent agents. Recent studies have shown that human/pedestrian trajectory prediction models are vulnerable to adversarial attacks. However, previous studies failed to consider the practicality of perturbations, generating adversarial trajectory with the length equal to the input length required by the prediction model. It is hard for an attacker (signed as candidate agent) to precisely walk on such many nodes in adversarial trajectory to execute an attack. Moreover, when selecting a target agent to approach for candidate agent, the attack is based solely on the distance to the candidate agent, ignoring the target agents with high relative velocity. This paper proposes a two-stage attack called AGNS aimed at minimizing the number of perturbing trajectory nodes meanwhile keeping the attack effectiveness. In the first stage, we propose a node-selection method to select the "important" nodes to reduce the number of nodes that we need to perturb. In the second stage, the selected nodes are perturbed with the attention-guided loss to generate a partial adversarial trajectory. Experiments on four models demonstrate that AGNS causes an average collision rate of over 80% while perturbing only 56% of the trajectory nodes, and even achieves an average collision rate exceeding 70% when perturbing just a single node. Our code can be obtained on Github: https://anonymous.4open.science/r/AGNS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".