# Smartphone Sensors in Motion: Advancing Traffic Safety with Mobile Technology
## 1 Introduction
The system has been validated through simulations in Car Sim and MATLAB/Simulink, showing how artificial intelligence can improve the interaction between vehicle operation and occupant comfort. In addition, another study [22] investigated the effect of brake pedal force on braking distance in heavy vehicles in wet conditions, using multi-body dynamics simulation to identify the non-linear relationship between brake pedal force and braking distance. This research highlights the critical influence of pedal force on braking deceleration, noting, in particular, its interaction with vehicle speed and weight, which is crucial for improving road safety in severe conditions. Recent studies in the field of human factors and traffic safety offer fundamental insights into how human perception and vehicle behavior affect safety. [PERSON] et al. [23] investigated how pedestrians perceive vehicle deceleration as a communication signal from automated vehicles, finding that faster deceleration at lower speeds and the presence of larger vehicles significantly improve the clarity of intent to stop, with this perception remaining consistent across different lighting conditions. [PERSON] and colleagues [24] further investigated the factors influencing stopping distance (SSD) in highway design, noting that deceleration rate, perception, and reaction time vary with different surface conditions, vehicle types, and driving scenarios. This research highlights the importance of incorporating different reaction times and deceleration rates into highway design to better account for real-world driving conditions and improve road safety. Recent advancements in vehicle navigation and safety systems leverage deep learning, neural network controllers, personalized deceleration models, and sensor data fusion techniques. [PERSON] and colleagues [25] introduced a deep learning method integrating GNSS, IMU, barometer, and DeepOdometry data via an Extended Kalman Filter, achieving accurate pitch and heading estimations within one degree, enhancing navigation in GNSS-denied areas. [PERSON] et al. [26] developed a neural network controller for electric vehicle braking, blending electrical and friction brakes to maximize energy recovery and outperforming traditional PID controllers in braking performance and energy efficiency. A study [27] on deceleration prediction models tailored to individual drivers improved regenerative braking by adapting to unique braking behaviors, enhancing comfort and efficiency. [PERSON], [PERSON], and [PERSON] [28] emphasized the importance of sensor data fusion, integrating information from various sensors to enhance vehicle safety systems and proposing future integration with wireless communications. Mobile technology is undergoing a revolution with the advent of affordable systems designed to monitor and enhance driver behavior. This is particularly important in developing nations, where road safety is a pressing concern. Md. [PERSON], [PERSON], and Dr. [PERSON] [29] have developed a system that utilizes smartphones equipped with accelerometers and GPS to identify rough driving actions. [PERSON], [PERSON], and [PERSON] [30] developed a mobile application that employs GPS, accelerometers, and microphones to detect driving behaviors and support law enforcement efforts. [PERSON], [PERSON], and [PERSON] [31] designed a driving behavior recognition system using motion sensors and a Kalman filter, achieving an accuracy rate of 93.25%. These studies demonstrate the significant potential of mobile technologies in promoting road safety. Compared to these studies, our work focuses on the measurement of deceleration under different weather and road conditions. Our results show that smartphones can provide reliable data even in more challenging conditions, expanding the possibilities of their real-world applications.