CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation (2110.13939v3)

Abstract: Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems. However, the diversity of scenarios and efficiency of generation methods are heavily restricted by the rareness and structure of safety-critical scenarios. Therefore, existing generative models that only estimate distributions from observational data are not satisfying to solve this problem. In this paper, we integrate causality as a prior into the scenario generation and propose a flow-based generative framework, Causal Autoregressive Flow (CausalAF). CausalAF encourages the generative model to uncover and follow the causal relationship among generated objects via novel causal masking operations instead of searching the sample only from observational data. By learning the cause-and-effect mechanism of how the generated scenario causes risk situations rather than just learning correlations from data, CausalAF significantly improves learning efficiency. Extensive experiments on three heterogeneous traffic scenarios illustrate that CausalAF requires much fewer optimization resources to effectively generate safety-critical scenarios. We also show that using generated scenarios as additional training samples empirically improves the robustness of autonomous driving algorithms.

• The paper introduces a causally-guided autoregressive framework that efficiently generates rare safety-critical driving scenarios.
• The methodology employs innovative causal masking with Behavioral Graphs and Autoregressive Flow to ensure adherence to key causal relationships.
• Experimental results show that CausalAF reduces optimization resources and enhances AV robustness by supplementing training with generated scenarios.

Integrating Causality into Generative Models for Efficient Safety-Critical Scenario Generation in Autonomous Driving

Introduction to Causal Generative Modeling

In the development and testing of autonomous vehicles (AVs), the generation of safety-critical scenarios is paramount. These scenarios, although rare, are crucial in evaluating an AV's robustness before deployment. Traditional deep generative models (DGMs), while effective in estimating distributions of observational data, often fall short in efficiently generating these rare scenarios due to their reliance on unstructured data and a lack of consideration for the underlying causal mechanisms. This paper introduces a novel approach, the Causal Autoregressive Flow (CausalAF), which embeds causality into the scenario generation process, significantly enhancing generation efficiency and effectiveness.

The Core of CausalAF

CausalAF is a structured generative model that employs causal graphs to guide the generation process. It introduces innovative causal masking operations to ensure that the generated scenarios adhere to predetermined causal relationships. By aligning the generative process with these causal relationships, CausalAF can generate safety-critical scenarios with higher efficiency and fidelity. This is achieved through two primary components:

1. Behavioral Graphs (BG): A new representation for traffic scenarios that captures interactions between objects using a directed graph structure.
2. Autoregressive Flow Model (AF): Utilizes the autoregressive property to generate the scenario graphs, ensuring that each node and edge in the behavioral graph is generated sequentially in accordance with the causal relationships defined by the causal graph.

The causal integration is further detailed through two specific mask types:

• Causal Order Masks (COM): Dictate the sequence of node generation to respect causal order derived from the causal graph.
• Causal Visibility Masks (CVM): Filter out irrelevant information during edge generation, allowing for causality-centric scenario construction.

Experimental Validation and Impact

Extensive experiments across three heterogeneous traffic scenarios show that CausalAF requires significantly fewer optimization resources than traditional data-driven baselines, demonstrating its efficiency. Furthermore, employing generated scenarios as additional training samples has been empirically shown to enhance the robustness of existing AV algorithms.

The implications of this research are twofold. Practically, it presents a more efficient and theoretically grounded method for the generation of rare safety-critical scenarios in autonomous driving. Theoretically, it lays down the groundwork for the integration of causality in generative modeling, opening up avenues for further exploration in this direction.

Future Directions and Limitations

While CausalAF marks a significant advancement in scenario generation for autonomous driving, there are limitations and areas for future exploration. One key limitation is the assumption that the causal graph, as determined by human experts, is always correct. Future work could explore automated methods for discovering or refining these causal relationships directly from data. Additionally, the application of this approach beyond autonomous driving, to other domains requiring efficient generation of rare scenarios, presents an exciting avenue for research.

Conclusion

CausalAF represents a pivotal step towards incorporating causality into deep generative models, specifically tailored for the efficient generation of safety-critical scenarios in autonomous driving. By doing so, it not only improves upon the generation task but also contributes to the broader understanding and application of causality in artificial intelligence.