Improving the Generalization of End-to-End Driving through Procedural Generation (2012.13681v2)

Abstract: Over the past few years there is a growing interest in the learning-based self driving system. To ensure safety, such systems are first developed and validated in simulators before being deployed in the real world. However, most of the existing driving simulators only contain a fixed set of scenes and a limited number of configurable settings. That might easily cause the overfitting issue for the learning-based driving systems as well as the lack of their generalization ability to unseen scenarios. To better evaluate and improve the generalization of end-to-end driving, we introduce an open-ended and highly configurable driving simulator called PGDrive, following a key feature of procedural generation. Diverse road networks are first generated by the proposed generation algorithm via sampling from elementary road blocks. Then they are turned into interactive training environments where traffic flows of nearby vehicles with realistic kinematics are rendered. We validate that training with the increasing number of procedurally generated scenes significantly improves the generalization of the agent across scenarios of different traffic densities and road networks. Many applications such as multi-agent traffic simulation and safe driving benchmark can be further built upon the simulator. To facilitate the joint research effort of end-to-end driving, we release the simulator and pretrained models at https://decisionforce.github.io/pgdrive

• The paper demonstrates that procedural generation significantly increases training diversity and improves RL agent generalization.
• The authors utilize the PGDrive simulator, built on Panda3D and Bullet, to create high-fidelity, varied driving scenarios.
• Experimental results confirm that enhanced environmental diversity leads to superior performance on unseen maps.

Improving the Generalization of End-to-End Driving through Procedural Generation

The presented paper addresses a critical challenge in end-to-end autonomous driving: the ability of driving agents to generalize to new, unseen environments. The research introduces PGDrive, a novel driving simulator that employs procedural generation (PG) to create a diverse array of driving scenarios. By increasing environmental diversity, the authors aim to enhance the generalization capabilities of reinforcement learning (RL) agents, which are typically susceptible to overfitting when trained on limited datasets.

Key Contributions

1. Procedural Generation for Training Diversity: PGDrive integrates PG techniques to construct a wide variety of maps using elementary road blocks like Straight, Ramp, Fork, and Roundabout, among others. This approach contrasts with existing simulators which have a limited and fixed pool of scenarios. The procedural methodology enhances map diversity, allowing more comprehensive evaluation and training datasets.
2. Simulated Environment and Features: Built on Panda3D and the Bullet engine, PGDrive offers high-fidelity simulations with detailed physics and customizable configurations like traffic density, vehicle dynamics, and road conditions. It also supports diverse data collection through Lidar and camera sensors, which are essential for modern autonomous systems.
3. Evaluation of Generalization: The paper empirically demonstrates that reinforcement learning agents exhibit poor generalization when restricted to a fixed set of scenarios but show significant improvement when exposed to procedurally generated environments. The experimental setup involved training agents using both on-policy (PPO) and off-policy (SAC) algorithms across varying numbers of procedurally generated maps, revealing that increased environmental diversity consistently improves test performance on unseen maps.
4. Practical Implications: By confirming the hypothesis that training data diversity improves generalization, this paper highlights vital considerations for the development of autonomous driving systems. The findings suggest that future AI systems can benefit from procedural generation for more robust and adaptable solutions in unstructured real-world environments.

Experimental Insights

The paper presents compelling evidence that agents trained in procedurally generated environments outperform those trained in fixed environments in terms of success rate and generalization across various conditions. The experiments also explore factors affecting generalization, such as traffic density and terrain friction, demonstrating that varied training conditions can lead to superior adaptability in agents.

Moreover, the procedural generation approach is contrasted with more traditional training paradigms, revealing that specialized agents handling homogeneous environments underperform when facing complex, multi-block scenarios. This reinforces the necessity for comprehensive testing frameworks incorporating dynamic and unpredictable variables.

Theoretical and Practical Implications

The research contributes to theoretical advancements by reinforcing the value of diversity in training data for generalization in RL, aligning with broader machine learning principles regarding variance and model robustness. Practically, it positions PGDrive as an essential tool for autonomous vehicle development, allowing researchers to simulate critical conditions that are otherwise difficult, dangerous, or costly to reproduce in the real world.

Future Directions

Speculation on future developments includes extending the PGDrive framework to accommodate distributed training, enabling parallel simulations to enhance data throughput and model convergence speed. Additionally, integrating more sophisticated PG algorithms could yield even more realistic and challenging environments, fostering better preparedness for real-world deployment.

In conclusion, the paper marks a significant step towards achieving efficient generalization in autonomous driving via procedural generation, highlighting both methodological innovations and experimental validations that can guide future developments in artificial intelligence and robotics.