Improving Robustness in Trajectory Prediction for Autonomous Vehicles: A Comprehensive Survey

核心概念

This paper presents a comprehensive framework for evaluating and improving the robustness of trajectory prediction models for autonomous vehicles, covering various strategies and methods to address the challenges of overfitting, adversarial attacks, and natural perturbations.

要約

The paper starts by formalizing the problem of trajectory prediction and defining the concept of robustness in this context. It then introduces a general framework that covers existing strategies for evaluating and improving the robustness of trajectory prediction models.

The evaluation strategies include:

1. Test on sliced data (E.1): Assessing robustness against overfitting by testing the model on specific subsets of the data, such as safety-critical scenarios or geographical locations.
2. Test on perturbed data (E.2): Evaluating the model's adversarial robustness by introducing perturbations to the test data.

The improvement strategies include:

1. Slice the training set differently (I.1): Modifying the training data to prevent overfitting and improve the model's performance on specific scenarios.
2. Add perturbed data to training set (I.2): Incorporating perturbations into the training data to enhance the model's robustness against spurious features.
3. Change the model architecture (I.3): Modifying the model architecture to improve the extraction of relevant features and mitigate overfitting.
4. Change the trained model (I.4): Refining the trained model post-training, such as through model compression techniques, to enhance its generalization capabilities.

The paper then provides detailed insights into specific methods for each of the evaluation and improvement strategies, including scenario importance sampling, geographic importance sampling, agent removal, geometric transformations, adversarial attacks/training, and contextual map perturbations.

Finally, the paper discusses recent developments in the field, highlights key research gaps, and suggests potential future research directions to advance the robustness of trajectory prediction models for autonomous vehicles.

統計

"Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance."
"Many deep learning models are known to be sensitive to small errors and susceptible to external attacks, potentially resulting in undesirable behavior and decreased performance."
"Robustness against spurious features in trajectory prediction can be categorized into adversarial and natural robustness."
"Assessing the resistance to overfitting can be measured through the train/test split, where the key is to determine whether the training and test data are identical and independent samples from the same data distribution."

引用

"Robustness against spurious features in trajectory prediction can be categorized into adversarial and natural robustness."
"Assessing the resistance to overfitting can be measured through the train/test split, where the key is to determine whether the training and test data are identical and independent samples from the same data distribution."

抽出されたキーインサイト

A survey on robustness in trajectory prediction for autonomous vehicles

by Jeroen Hagen... 場所 arxiv.org 04-23-2024

https://arxiv.org/pdf/2402.01397.pdf

深掘り質問

How can the proposed robustness evaluation and improvement strategies be combined and optimized to achieve a synergistic effect in enhancing the reliability of trajectory prediction models

To achieve a synergistic effect in enhancing the reliability of trajectory prediction models, the proposed robustness evaluation and improvement strategies can be combined and optimized in a strategic manner. One approach is to integrate data slicing methods with perturbation techniques. By slicing the training set differently to focus on specific scenarios and then adding perturbed data to the training set, the model can be trained on a more diverse and challenging dataset. This combination allows the model to learn from a wide range of scenarios, including rare and safety-critical events, while also being exposed to perturbations that mimic real-world uncertainties.
Furthermore, changing the model architecture can complement these strategies by enhancing the model's ability to extract relevant features and understand complex interactions between agents. By modifying the building blocks of the deep learning model to improve its robustness against spurious features, the model can better generalize to new environments and handle adversarial attacks effectively.
Regular evaluation using performance measures such as Average Displacement Error and Final Displacement Error can help track the model's progress and ensure that improvements in robustness do not compromise predictive accuracy. By continuously iterating on these strategies and incorporating feedback from evaluation metrics, a trajectory prediction model can be optimized to achieve a balance between robustness and accuracy, ultimately enhancing its reliability in real-world applications.

What are the potential trade-offs between improving robustness and maintaining high predictive accuracy, and how can these be effectively balanced

The potential trade-offs between improving robustness and maintaining high predictive accuracy in trajectory prediction models revolve around finding the right balance between these two objectives. Enhancing robustness often involves introducing perturbations, changing the model architecture, and modifying the training data to make the model more resilient to uncertainties and adversarial attacks. While these strategies can improve the model's performance in challenging scenarios, they may also impact its predictive accuracy on benign data.
One way to effectively balance these trade-offs is through careful experimentation and validation. By conducting thorough testing on both perturbed and unperturbed data, the model's performance can be evaluated under different conditions. This iterative process allows for the identification of optimal parameters and strategies that enhance robustness without significantly compromising accuracy.
Additionally, leveraging techniques like adversarial training can help the model learn to defend against attacks while maintaining high accuracy on clean data. Adversarial training exposes the model to adversarial examples during training, forcing it to learn robust features that generalize well to unseen scenarios. By incorporating a diverse range of training scenarios and continuously monitoring performance metrics, the model can strike a balance between robustness and accuracy.

How can the insights from this survey on trajectory prediction be extended to other safety-critical domains, such as robotics or medical diagnosis, to improve the robustness of AI systems in those fields

The insights from this survey on trajectory prediction can be extended to other safety-critical domains, such as robotics or medical diagnosis, to improve the robustness of AI systems in those fields. In robotics, for example, trajectory planning and motion prediction are essential for autonomous navigation and interaction with the environment. By applying similar robustness evaluation and improvement strategies, robotics systems can be made more resilient to uncertainties, sensor noise, and adversarial attacks.
In the context of medical diagnosis, AI systems are increasingly being used for tasks like image analysis, patient monitoring, and disease prediction. Ensuring the robustness of these systems is crucial for accurate diagnosis and treatment planning. By incorporating techniques like data slicing, perturbation methods, and model architecture changes, medical AI systems can be made more reliable and trustworthy in real-world healthcare settings.
Overall, the principles of enhancing robustness in AI systems, as highlighted in the survey on trajectory prediction, can be generalized to various safety-critical domains to improve the overall reliability and performance of AI applications in critical scenarios.