An Adaptive Sequencing Approach for Object Detection in Autonomous Vehicles

The integration of autonomous vehicles into our daily lives is steadily advancing, with researchers and institutions contributing to the realization of fully autonomous commercially available vehicles. Among the critical components of such vehicles is object detection, which draws from various fields like image processing and statistics. This article introduces a novel real-time adaptive object detection method inspired by the principles of real-time computing and control systems, meticulously tailored for use in automated vehicle control systems. In this context, it is essential to recognize that computational resources are limited, and this limitation has a direct impact on the reaction time. To address this challenge, our method leverages the aggregate channel features (ACF) detection algorithm, thoughtfully incorporating considerations of computational resources by integrating feedback from the vehicle motion planner. The proposed model undergoes comprehensive analysis and simulation within the MATLAB and Simulink environment, and the results are indeed promising, showcasing significant enhancements in the reaction time.