CMTS: Conditional Multiple Trajectory Synthesizer for Generating Safety-critical Driving Scenarios
CMTS uses variational Bayesian latent space interpolation and style transfer to generate diverse near-miss driving scenarios, improving trajectory prediction robustness.
Key Findings
Methodology
The proposed CMTS framework integrates variational autoencoder (VAE) with conditional map encoding, employing latent space linear interpolation and AdaIN style transfer to synthesize safety-critical scenarios. It encodes safe and collision trajectories into a shared latent space, conditioned on road maps, and interpolates between their distributions. The model consists of a sequential trajectory encoder (GRU), a convolutional map encoder, and a decoder. During training, the model maximizes ELBO with regularization to ensure smooth, realistic trajectory generation. Style transfer separates road conditions from driving behaviors, enabling flexible scenario synthesis. The approach effectively bridges safe and collision data, producing diverse near-miss scenarios for training and evaluation.
Key Results
- On the Argoverse dataset, CMTS generated a richer set of near-miss trajectories, increasing the number of identifiable clusters (K) from 53 to 71, indicating enhanced diversity. Trajectory smoothness metrics showed improvements comparable to ACAI, with mean distance and smoothness scores decreasing by 20-30%. In predictive tasks, models trained with augmented data achieved a 55% reduction in MSE (from 0.093 to 0.042) and a 30% increase in MDV scores across six risky scenarios, demonstrating improved robustness in dangerous situations.
- Analysis with Dirichlet Process Gaussian Mixture Model (DPGMM) confirmed that CMTS-generated data encompass more complex traffic behaviors, with higher cluster counts than original or collision datasets. Visualization via t-SNE revealed that interpolated trajectories line up coherently in latent space, confirming the model’s ability to produce plausible, continuous scenarios.
- In trajectory prediction experiments, models trained with CMTS data outperformed baselines (Vanilla-LSTM, Social-LSTM) in MSE, MDV, and MDN metrics, especially in risky scenarios, validating the practical utility of synthetic near-miss data for safety-critical applications.
Significance
This work addresses the critical challenge of data scarcity in risky traffic scenarios, offering a scalable method to generate diverse, realistic near-miss trajectories. By enriching training datasets with synthetic but plausible dangerous scenarios, it enhances the robustness and safety of autonomous driving algorithms. The approach bridges the gap between safe driving data and collision scenarios, facilitating better generalization and safety validation. Its ability to produce controllable, multimodal scenarios opens new avenues for simulation, testing, and certification of autonomous systems, ultimately contributing to safer deployment of autonomous vehicles in complex environments.
Technical Contribution
The key technical innovation lies in combining variational latent space interpolation with style transfer to disentangle road conditions from driving behaviors. The model’s architecture allows for smooth, multimodal scenario synthesis, with explicit control over road styles via AdaIN. The regularization strategy ensures the generated trajectories are continuous and realistic. Unlike prior works limited to static data augmentation, CMTS enables dynamic, multi-domain scenario generation with high diversity and fidelity. The theoretical guarantee of latent space interpolation and the practical implementation of style-conditioned generation mark significant advances in traffic scene modeling.
Novelty
This research is the first to apply latent space interpolation combined with style transfer for traffic scenario synthesis, specifically targeting near-miss and risky situations. Unlike previous methods that generate static or simplistic trajectories, CMTS creates continuous, multimodal scenarios that smoothly transition between safe and collision states. Its ability to control road conditions as a style attribute, and generate diverse, realistic risk scenarios, sets it apart from existing data augmentation and generative approaches in autonomous driving. This innovation opens new possibilities for safety testing and scenario customization.
Limitations
- The current model does not incorporate vehicle kinematic or dynamic constraints, which may result in physically implausible trajectories, limiting real-world applicability without further refinement.
- While latent space interpolation enhances diversity, extreme or highly complex risk scenarios remain challenging to generate accurately, potentially missing rare but critical events.
- Training relies heavily on regularization and style transfer, which can introduce biases or artifacts, affecting trajectory realism and stability. Future work should integrate physical constraints and multi-modal control to improve fidelity.
Future Work
Future directions include embedding vehicle physics and kinematic constraints into the generative process to produce physically feasible trajectories. Extending the model to incorporate multi-modal conditional controls will enable more precise scenario customization. Additionally, integrating reinforcement learning could optimize generated trajectories for safety and efficiency. Exploring real-time scenario generation for online testing and expanding to multi-agent interactions are promising avenues to further enhance the robustness and applicability of this approach.
AI Executive Summary
Autonomous driving relies heavily on diverse traffic scene data, yet existing datasets predominantly feature safe driving behaviors, leaving a significant gap in risky and near-miss scenarios. This deficiency hampers the development of robust algorithms capable of handling complex, dangerous situations. Addressing this challenge, the study introduces CMTS, a novel generative framework that synthesizes safety-critical traffic scenarios by leveraging variational Bayesian latent space interpolation and style transfer techniques.
The core idea involves encoding safe and collision trajectories into a shared latent space conditioned on road maps, then interpolating between their distributions to generate intermediate, near-miss scenarios. The model employs a combination of GRU-based trajectory encoders, convolutional map encoders, and a decoder, with AdaIN style transfer to disentangle road conditions from driving behaviors. During training, the model maximizes the evidence lower bound (ELBO), incorporating regularization to ensure smooth, realistic trajectory synthesis.
Experimental results on the Argoverse dataset demonstrate that CMTS significantly enriches the diversity of generated scenarios, increasing cluster counts and improving trajectory smoothness. Quantitative metrics show a 55% reduction in prediction error (MSE) and a 30% boost in robustness across risky scenarios, validating the method’s effectiveness. The synthetic data not only enhances trajectory prediction accuracy but also broadens the scope of safety validation, enabling autonomous systems to better anticipate and respond to rare, hazardous events.
This approach offers a scalable, controllable way to generate realistic traffic scenarios, bridging the gap between safe driving data and dangerous situations. Its potential impact extends to safer autonomous vehicle deployment, improved testing protocols, and more resilient AI systems. Future work aims to incorporate vehicle physics, multi-modal controls, and real-time scenario generation, further advancing the field of traffic scene simulation and safety assurance.
Deep Dive
Abstract
Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss scenarios that rarely show up in off-the-shelf datasets is a vital part of the evaluation. As a remedy, we propose a near-miss data synthesizing framework based on Variational Bayesian methods and term it as Conditional Multiple Trajectory Synthesizer (CMTS). We leverage a generative model conditioned on road maps to bridge safe and collision driving data by representing their distribution in the latent space. By sampling from the near-miss distribution, we can synthesize safety-critical data crucial for understanding traffic scenarios but not shown in neither the original dataset nor the collision dataset. Our experimental results demonstrate that the augmented dataset covers more kinds of driving scenarios, especially the near-miss ones, which help improve the trajectory prediction accuracy and the capability of dealing with risky driving scenarios.