Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation

TL;DR

This paper proposes a principled, simulator-agnostic domain adaptation approach to improve perception models for autonomous driving by minimizing the reality gap using neural-invariant representations and optimized data sampling.

Contribution

It introduces a novel, theoretically inspired method for training self-driving perception models in simulation that reduces the domain gap without requiring real-world labels.

Findings

Effective reduction of the reality gap in perception models

Identification of key variations affecting model performance

Method is easy to implement and simulator-agnostic

Abstract

Autonomous driving relies on a huge volume of real-world data to be labeled to high precision. Alternative solutions seek to exploit driving simulators that can generate large amounts of labeled data with a plethora of content variations. However, the domain gap between the synthetic and real data remains, raising the following important question: What are the best ways to utilize a self-driving simulator for perception tasks? In this work, we build on top of recent advances in domain-adaptation theory, and from this perspective, propose ways to minimize the reality gap. We primarily focus on the use of labels in the synthetic domain alone. Our approach introduces both a principled way to learn neural-invariant representations and a theoretically inspired view on how to sample the data from the simulator. Our method is easy to implement in practice as it is agnostic of the network architecture and the choice of the simulator. We showcase our approach on the bird's-eye-view vehicle segmentation task with multi-sensor data (cameras, lidar) using an open-source simulator (CARLA), and evaluate the entire framework on a real-world dataset (nuScenes). Last but not least, we show what types of variations (e.g. weather conditions, number of assets, map design, and color diversity) matter to perception networks when trained with driving simulators, and which ones can be compensated for with our domain adaptation technique.