Towards Infant Sleep-Optimized Driving: Synergizing Wearable and Vehicle Sensing in Intelligent Cruise Control

TL;DR

This paper presents an intelligent cruise control system that uses reinforcement learning and neural networks to optimize driving behavior for better infant sleep quality by integrating wearable and vehicle data.

Contribution

It introduces a novel RL-based framework that dynamically adjusts driving aggressiveness to enhance infant sleep, combining wearable sensor data with vehicle and map information.

Findings

Significant improvement in infant sleep quality in simulations

Effective synergy of wearable sensors and vehicle data

Maintained travel efficiency despite sleep optimization

Abstract

Automated driving (AD) has substantially improved vehicle safety and driving comfort, but their impact on passenger well-being, particularly infant sleep, is not sufficiently studied. Sudden acceleration, abrupt braking, and sharp maneuvers can disrupt infant sleep, compromising both passenger comfort and parental convenience. To solve this problem, this paper explores the integration of reinforcement learning (RL) within AD to personalize driving behavior and optimally balance occupant comfort and travel efficiency. In particular, we propose an intelligent cruise control framework that adapts to varying driving conditions to enhance infant sleep quality by effectively synergizing wearable sensing and vehicle data. Long short-term memory (LSTM) and transformer-based neural networks are integrated with RL to model the relationship between driving behavior and infant sleep quality under diverse traffic and road conditions. Based on the sleep quality indicators from the wearable sensors, driving action data from vehicle controllers, and map data from map applications, the model dynamically computes the optimal driving aggressiveness level, which is subsequently translated into specific AD control strategies, e.g., the magnitude and frequency of acceleration, lane change, and overtaking. Simulation experiments conducted in the CARLA environment indicate that the proposed solution significantly improves infant sleep quality compared to baseline methods, while preserving desirable travel efficiency.