Robust Behavioral Cloning for Autonomous Vehicles using End-to-End Imitation Learning (2010.04767v4)

Abstract: In this work, we present a lightweight pipeline for robust behavioral cloning of a human driver using end-to-end imitation learning. The proposed pipeline was employed to train and deploy three distinct driving behavior models onto a simulated vehicle. The training phase comprised of data collection, balancing, augmentation, preprocessing and training a neural network, following which, the trained model was deployed onto the ego vehicle to predict steering commands based on the feed from an onboard camera. A novel coupled control law was formulated to generate longitudinal control commands on-the-go based on the predicted steering angle and other parameters such as actual speed of the ego vehicle and the prescribed constraints for speed and steering. We analyzed computational efficiency of the pipeline and evaluated robustness of the trained models through exhaustive experimentation during the deployment phase. We also compared our approach against state-of-the-art implementation in order to comment on its validity.

Robust Behavioral Cloning for Autonomous Vehicles Using End-to-End Imitation Learning: A Summary

The subject paper introduces a lightweight pipeline for robust behavioral cloning using end-to-end imitation learning, specifically aimed at autonomous vehicles. The researchers, Tanmay Vilas Samak, Chinmay Vilas Samak, and Sivanathan Kandhasamy, address the challenges of autonomous driving by proposing a streamlined methodology that integrates perception, planning, and control into a singular learning process.

Approach and Methodology

The presented pipeline is utilized to train and deploy models for three distinct driving behaviors: simplistic driving, rigorous driving, and collision avoidance. It consists of several stages, including data collection, balancing, augmentation, preprocessing, and training of a neural network. The ultimate goal is to facilitate the ego vehicle's navigation by predicting steering commands from camera inputs.

Notably, the pipeline incorporates a novel coupled control law for longitudinal control, which operates in conjunction with the model's steering predictions. This is achieved by fusing the predicted steering with real-time vehicle telemetry to produce throttle and brake commands. The system's robustness is validated via a comprehensive set of experiments that test the trained models under various environmental perturbations.

Key Results and Findings

The experimental results denote the efficacy of the pipeline in both computational efficiency and robustness. Training times for the behavior models ranged from 1.4 to 10.9 hours, markedly less than other state-of-the-art implementations. Additionally, the deployment latency was consistently between 1.5 to 3 milliseconds, indicating the system's capability for real-time application.

The robustness tests revealed different levels of success across the driving behaviors. Collision avoidance exhibited the highest degree of robustness, maintaining performance across varying conditions. The results for rigorous driving and simplistic driving demonstrated some susceptibility to changes, such as light variation and orientation shifts.

The researchers conducted a comparative analysis with NVIDIA's PilotNet architecture, using their proposed pipeline. The trained models using the proposed method displayed marginally better robustness and comparable training times relative to NVIDIA's approach, attesting to the improved generalization achieved without excessively increasing computational demands.

Implications and Future Directions

The findings of this research underscore the potential of a lightweight, end-to-end approach to enhance autonomous vehicle capabilities. By focusing on robust performance across varying conditions, this methodology can contribute to improved reliability in real-world autonomous driving applications.

Continued research could explore hardware implementations and real-world applications (sim2real) of the proposed pipeline. Additionally, extending the dataset through multi-driver inputs or varied sensing modalities (e.g., LiDAR) could further improve model generalization. Future studies might address the inherent generalization failure in complex and dynamic scenarios, potentially through hybrid models combining end-to-end learning with modular approaches.

The paper also opens avenues for standardizing experimental and evaluation metrics for imitation learning pipelines in autonomous driving, thereby contributing to the broader discourse on end-to-end learning's viability in safety-critical systems.