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Improving Robustness of Vehicle Motion Prediction Models by Leveraging Frenet Coordinates


Core Concepts
Representing vehicle motion prediction models in the Frenet coordinate frame significantly improves their robustness to challenging scenarios, without sacrificing average performance.
Abstract
The paper presents a general wrapper that integrates state-of-the-art vehicle motion prediction models into a Frenet coordinate frame representation. This approach aims to address the problem of learning-based prediction models producing off-road trajectories in challenging scenarios, where the road topology ahead of the vehicle is perturbed while the motion history remains unchanged. The key highlights are: The Frenet frame wrapper can be applied to various state-of-the-art prediction models without modifying their architecture. This allows studying the tradeoffs between prediction accuracy and generalization that the Frenet representation induces. Experiments on the Argoverse dataset and the scene-attack benchmark show that the Frenet wrapper reduces off-road predictions by over 90% in challenging perturbed scenarios, compared to the original Cartesian models. The Frenet wrapper also provides the benefit of more diverse predictions, increasing the mean inter-endpoint distance by 20%, at the cost of a slight decrease in prediction accuracy on original scenarios. The authors demonstrate that simple techniques like adding dropouts to the motion history or removing it entirely do not improve the robustness of the Cartesian models, unlike the Frenet wrapper approach. The paper also includes an extensive ablation study on different methods for aggregating predictions from multiple relevant lane centerlines in the Frenet frame. Overall, the work presents a general and effective solution to improve the robustness of vehicle motion prediction models by leveraging the Frenet coordinate representation.
Stats
The off-road probability (ORP) of the Cartesian models increases from 0.7% to 63.9% and from 1.7% to 67.1% in the Double Turn perturbation for LaneGCN and Multipath++, respectively. In contrast, the ORP of the Frenet wrapper models only increases by 2.5 percentage points for both models. The Frenet wrapper reduces the minimum Average Displacement Error (minADE) on perturbed scenes by over 50% compared to the Cartesian models.
Quotes
"Recent work specifically targets trajectory prediction algorithms. In [14], an adversarial attack on trajectory prediction based on perturbations of the neighbouring vehicle's trajectory is proposed, resulting in dramatically increased prediction errors." "Similarly, [15] shows that off-road predictions increase when the road ahead is perturbed with a curve, while the motion history and the road behind are left unchanged."

Deeper Inquiries

How can the Frenet wrapper be extended to handle more complex road topologies, such as intersections and merging lanes?

The Frenet wrapper can be extended to handle more complex road topologies by incorporating additional information about lane connections, intersections, and merging lanes into the Frenet frame representation. When dealing with intersections, the Frenet frame can be adapted to include multiple possible paths and lane changes that a vehicle can take. By identifying relevant lane centerlines at these critical points, the Frenet wrapper can provide a more comprehensive view of the road scenario. This extension would involve enhancing the lane identification process to accurately capture all possible trajectories at intersections and merging lanes. Additionally, the prediction model within the Frenet wrapper can be trained to understand and predict the behavior of vehicles in these complex scenarios, taking into account factors like right-of-way rules and lane changes.

What are the computational and memory trade-offs of the Frenet wrapper compared to the original Cartesian models?

The Frenet wrapper introduces computational and memory trade-offs compared to the original Cartesian models. The transformation of inputs and outputs into the Frenet frame requires additional processing steps, which can increase computational overhead. This transformation involves converting coordinates, calculating curvatures, and mapping trajectories to the lane centerlines, which may result in higher computational costs. Furthermore, storing and processing information in the Frenet frame format may require more memory compared to the Cartesian representation, as it involves maintaining additional lane information and transformations. However, despite these trade-offs, the Frenet wrapper offers benefits in terms of prediction accuracy and generalization in challenging scenarios. The trade-offs in computational complexity and memory usage are justified by the improved performance and robustness of the prediction models when handling complex road scenarios. Overall, the Frenet wrapper strikes a balance between computational resources and predictive capabilities, making it a valuable tool for enhancing vehicle motion prediction models.

Can the Frenet representation be combined with other techniques, such as adversarial training or data augmentation, to further improve the robustness of vehicle motion prediction models?

Yes, the Frenet representation can be effectively combined with other techniques like adversarial training and data augmentation to enhance the robustness of vehicle motion prediction models. Adversarial training can be used to generate challenging scenarios that test the model's ability to predict trajectories accurately in the presence of perturbations or adversarial inputs. By incorporating adversarial examples based on the Frenet representation, the model can learn to make more robust predictions in real-world driving scenarios. Additionally, data augmentation techniques can be applied to the Frenet-transformed data to introduce variations and increase the diversity of training samples. This can help the model generalize better to different road conditions and driving scenarios. By augmenting the Frenet frame data with perturbed or modified trajectories, the model can learn to adapt to unexpected situations and improve its prediction accuracy. Overall, combining the Frenet representation with adversarial training and data augmentation techniques can lead to more robust and reliable vehicle motion prediction models that perform well in a wide range of driving conditions and scenarios.
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