[3M1-GS-10] AI application:

[3M1-GS-10-03] Evaluation of Deep Neural Network Using Stereo Camera in Adaptive Cruise Control

〇Kengo Hoi¹, Soichiro Yokoyama², Tomohisa Yamashita², Hidenori Kawamura², Hiroaki Kata³, Satoshi Kashiwamura³ (1. Graduate School of Information Science and Technology, Hokkaido University, 2. Faculty of Information Science and Technology, Hokkaido University, 3. Hitachi Astemo, Ltd.)

Keywords:Autonomous Driving, Deep Neural Network, Stereo Camera, Adaptive Cruise Control

In recent years, research on end-to-end autonomous driving systems has become active; however, high-precision inference requires high-performance machines for both training and deployment, which imposes significant burdens on research and commercialization. If stereo cameras are employed, then not only can depth estimation be omitted through algorithmic distance measurement, but also, by acquiring both image and distance information with a single sensor—potentially eliminating the need for sensor fusion—the model is expected to be simplified. In this study, as an initial investigation into an end-to-end autonomous driving model using stereo cameras, we develop a model for adaptive cruise control, a fundamental autonomous driving task. We modified the Transfuser model to predict vehicle acceleration from stereo camera data. Evaluation on roughly 160,000 frames from Japanese public roads showed high accuracy in monotonous environments with minimal acceleration or deceleration, suggesting the practical viability of this approach. Conversely, in conditions of significant acceleration/deceleration, uphill driving, or abrupt lighting changes, accuracy decreased; we analyzed the causes and proposed improvements.