Exploring the Causality of End-to-End Autonomous Driving

Exploring the Causality of End-to-End Autonomous Driving

Abstract The paper introduces a systematic approach to dissect and analyze the causality in deep learning-based end-to-end autonomous driving models. This research addresses the inherent "black-box" properties of these models, which obscure the understanding of their decision-making processes and raise safety concerns. Through a series of controlled experiments, qualitative and quantitative analyses, the paper develops a comprehensible framework for debugging autonomous driving models, revealed through an integrated tool, "DriveInsight," evaluated extensively using the CARLA simulator.

Introduction

In the realm of autonomous driving, the opacity of deep learning models poses substantial challenges for trust and safety. The authors underscore that for systems with such critical safety requirements, elucidating the causality behind model decisions is imperative. Previous attempts at making autonomous driving systems interpretable have not provided a comprehensive analytical framework. This paper fills this void by proposing a method that systematically analyzes the contributions of various factors within end-to-end autonomous driving models, offering a strong baseline for future investigación.

Methodology

The authors present DriveInsight, a comprehensive framework encompassing model architecture, controlled variable experiments, counterfactual interventions, and visualization techniques. The process involves the following steps:

1. Data Collection and Filtration: Using the CARLA simulator, the authors collected a dataset comprising 3.1 million frames, which was meticulously filtered to ensure data quality.
2. Training Strategy: A robust model training strategy is delineated, involving multi-view camera images, LiDAR input, and other sensor data, harnessing 32 A800 GPUs.

The core methodology involves breaking down the system into modular components, examining each independently, and utilizing qualitative and quantitative methods to assess their contributions to the final decision.

Ablation Studies and Qualitative Analysis

The paper presents an array of ablation studies to determine the significance of each input factor in the planning decoder:

• Current Speed: The model's predictions alter significantly when the current speed is perturbed, affecting the vehicle's acceleration and deceleration behaviors.
• Routing and Target Points: These components are shown to have a combined effect on guiding vehicle behavior.
• BEV Features: The absence of BEV features results in significant safety issues, underscoring the importance of environmental perception.

The counterfactual interventions further reveal how individual factors like map data, traffic lights, and BEV features can critically alter the model’s decisions, emulating real-world scenarios where these factors dynamically interact.

Visualization and Quantitative Analysis

The response analysis involves examining gradients relative to the model's output and visualizing attention weights within transformer layers:

• Gradient Analysis: Components such as traffic lights, stop signs, and BEV features exhibit distinct response patterns, crucially impacting decision-making during driving tasks.
• Activation Maps: The activation maps reveal the spatial distribution of key features within the model, emphasizing areas like lane lines, obstacles, and traffic signals.

Through these visualizations, the authors provide a concrete understanding of how different features influence the model's behavior, facilitating better interpretability and debugging.

Benchmarks and Comparative Results

The DriveInsight framework outperforms existing methods on the CARLA Town05 Long and Short benchmarks, achieving competitive driving scores and the highest route completion rates:

• Closed-Loop Evaluation consistently demonstrates DriveInsight's efficacy in diverse driving environments, showcasing robust handling of complex scenarios without relying on manual post-processing rules.

Conclusion

The research presents a pioneering debugging and analysis solution for end-to-end autonomous driving models, effectively transforming the black-box nature into a white one. DriveInsight sets a new benchmark for causality analysis in autonomous systems, offering valuable insights for enhancing model interpretability and safety. Future work may extend these findings to real-world driving conditions, further verifying the robustness and reliability of the approach.

Implications and Future Directions

Practically, this research facilitates the development of safer, more reliable autonomous driving systems by providing tools for comprehensive debugging and transparency. Theoretically, it lays the groundwork for future studies on model interpretability in AI, particularly within safety-critical domains. The authors propose extending their work to real-world driving environments, potentially paving the way for significant advancements in autonomous vehicle safety and trustworthiness.