Robust Vehicle Trajectory Prediction via Counterfactual Intervention for Autonomous Driving
Bibliographic record
Abstract
The trajectory prediction module of autonomous driving navigation systems is required to correctly forecast the future trajectories of other agents and make correct driving decisions to guarantee the safety of self-driving vehicles. However, the trajectory prediction module is susceptible to adversarial attacks, which can lead to wrong driving decisions. Here, we propose RVTP, a robust prediction method against adversarial attacks for trajectory prediction, which is grounded in causal inference. RVTP employs counterfactual intervention to remove the influence of adversarial perturbations on observed history trajectories. Firstly, we present four evaluation metrics to measure directed attacks from four directions under adversarial attacks. Secondly, we establish the causal graph in attack scenarios and study the causal relationships of various elements under attack. Thirdly, we implement the counterfactual intervention based on the causal graph to calculate the causal effect for mitigating the influence of adversarial attacks. Compared with other trajectory prediction methods under attack, extensive experiments demonstrate that RVTP achieves enhanced performance under attacks at the cost of a minimal performance decrease in no attack scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".