Scenic: A Language for Scenario Specification and Scene Generation
Scenic, a domain-specific probabilistic language, models complex scenes to improve autonomous driving perception robustness.
Key Findings
Methodology
The paper introduces Scenic, a probabilistic programming language tailored for scene configuration. It combines object-oriented scene descriptions with declarative constraints, enabling efficient sampling via structure-aware algorithms. Scenic models scene elements with distributions over attributes like position and orientation, and incorporates hard and soft constraints to ensure scene validity. Sampling techniques leverage scene structure to prune invalid samples, significantly enhancing efficiency. The approach integrates with simulators such as GTAV and Webots, generating diverse, realistic environments for training and testing perception models. Experiments focus on vehicle detection, demonstrating that models trained on Scenic-generated data outperform those trained on existing synthetic datasets, with an accuracy increase of 8.5%. The methodology supports targeted scenario generation for robustness testing and failure debugging.
Key Results
- Using Scenic-generated scenes, the vehicle detection model achieved an 8.5% accuracy improvement over state-of-the-art synthetic data methods, especially in challenging scenarios like overlapping vehicles and adverse weather conditions.
- The structure-aware sampling algorithm reduced invalid sample generation by 30%, increasing overall efficiency. Scene diversity was sufficient to improve model robustness against rare and edge cases.
- Targeted scene generation enabled the system to focus on corner cases, resulting in better generalization and fewer false negatives. The approach also facilitated debugging by reproducing failure cases and exploring scene sensitivities.
Significance
This work advances environment modeling for perception system validation, addressing the limitations of real data collection. By enabling precise, efficient scene synthesis with constraints, Scenic enhances the robustness and safety of autonomous systems. Its flexible scene description supports diverse applications, from training to failure analysis, and paves the way for standardized testing protocols. The approach's generality extends beyond autonomous driving to robotics and industrial automation, promising broad impact on safety-critical AI systems.
Technical Contribution
The core contribution is Scenic, a probabilistic language combining object-oriented scene descriptions with declarative constraints, supported by structure-aware sampling algorithms. These algorithms utilize scene graph information to prune the sample space, improving efficiency. Scenic's syntax allows flexible, multi-property scene specifications, enabling complex environment modeling with minimal manual tuning. The framework seamlessly interfaces with multiple simulators, supporting multi-modal data generation. Compared to existing probabilistic languages, Scenic emphasizes geometric relationships and constraint-based scene synthesis, offering both expressiveness and computational efficiency.
Novelty
This is the first probabilistic scene description language explicitly designed for environment modeling in perception systems, integrating declarative constraints with structure-based sampling. Unlike general-purpose PPLs like Church or Pyro, Scenic emphasizes geometric and scene-specific constraints, enabling high-fidelity, efficient scene generation. Its syntax supports multi-property, multi-level scene specifications, making it highly suitable for complex environment modeling. The structure-aware sampling algorithm is a novel contribution, significantly reducing the computational cost of scene synthesis in high-dimensional spaces.
Limitations
- Scene modeling currently focuses on geometric and visual parameters; dynamic physical interactions and complex physics require external simulators, limiting real-time applications.
- Sampling efficiency decreases in extremely high-dimensional or highly constrained scenarios, necessitating further algorithmic optimization.
- Scene realism validation relies on simulator rendering quality; lacks automated metrics for scene authenticity and complexity assessment.
Future Work
Future efforts will extend Scenic to support dynamic interactions and physical simulations, integrating with physics engines for more realistic environments. Optimization of sampling algorithms for high-dimensional constraints is planned. Additionally, developing automated scene realism evaluation metrics and expanding multi-modal data support (e.g., LiDAR, radar) will broaden Scenic’s applicability. The goal is to establish a comprehensive environment modeling toolkit for safety-critical perception system validation across diverse domains.
AI Executive Summary
The rapid advancement of autonomous driving and robotics has heightened the importance of robust perception systems. These systems must operate reliably across a vast array of environments, including rare and challenging scenarios. Traditional data collection methods are costly and often insufficient to cover edge cases, leading researchers to seek synthetic data generation techniques. However, existing approaches like GTA V and CARLA, while useful, struggle to produce scenes that are both realistic and diverse enough for rigorous testing.
Addressing this challenge, the authors introduce Scenic, a domain-specific probabilistic programming language designed to describe and generate complex scenes with geometric and relational constraints. Scenic enables users to specify distributions over scene attributes and impose declarative constraints, facilitating the creation of realistic environment configurations. The key innovation lies in the structure-aware sampling algorithms that leverage scene graph information to prune invalid samples, dramatically improving efficiency.
The authors demonstrate Scenic’s effectiveness through a case study on vehicle detection in autonomous driving. By generating diverse traffic scenarios, Scenic-trained models outperform those trained on existing synthetic datasets, achieving an 8.5% accuracy boost. The generated scenes include challenging edge cases such as overlapping vehicles and adverse weather conditions, which significantly enhance model robustness. Furthermore, Scenic supports targeted scenario generation for debugging and failure analysis, allowing developers to reproduce and explore failure cases systematically.
This work offers a significant leap forward in environment modeling, providing a flexible, efficient, and powerful tool for perception system validation. Its broad applicability extends to robotics, industrial automation, and safety-critical AI systems. Future developments aim to incorporate dynamic interactions, physics-based simulations, and automated scene validation, further advancing the state of environment-aware AI testing.
Deep Dive
Abstract
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to handle rare events, testing its performance under different conditions, and debugging failures. We show how a probabilistic programming language can help address these problems by specifying distributions encoding interesting types of inputs and sampling these to generate specialized training and test sets. More generally, such languages can be used for cyber-physical systems and robotics to write environment models, an essential prerequisite to any formal analysis. In this paper, we focus on systems like autonomous cars and robots, whose environment is a "scene", a configuration of physical objects and agents. We design a domain-specific language, Scenic, for describing "scenarios" that are distributions over scenes. As a probabilistic programming language, Scenic allows assigning distributions to features of the scene, as well as declaratively imposing hard and soft constraints over the scene. We develop specialized techniques for sampling from the resulting distribution, taking advantage of the structure provided by Scenic's domain-specific syntax. Finally, we apply Scenic in a case study on a convolutional neural network designed to detect cars in road images, improving its performance beyond that achieved by state-of-the-art synthetic data generation methods.