Real-Time Vehicle Roll Angle Estimation Based on Neural Networks in IoT Low-Cost Devices

Abstract

The high rate of vehicle-crash victims has a fatal economic and social impact in today’s societies. In particular, road crashes where heavy vehicles are involved cause more severe damage because they are prone to rollover. For this reason, many researches are focused on developing RSC Roll Stability Control (RSC) systems. Concerning the design of RSC systems with an adequate performance, it is mandatory to know the dynamics of the vehicle. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill hard real-time processing constraints, achieving high level of accuracy during circulation of a vehicle in real situations. In order to address this issue, this study has two main goals: (1) Design and develop an IoT based architecture, integrating ANN in low cost kits with different hardware architectures in order to estimate under real-time constraints the vehicle roll angle. This architecture is able to work under high dynamic conditions, by following specific best practices and considerations during its design; (2) assess that the IoT architecture deployed in low-cost experimental kits achieve the hard real-time performance constraints estimating the roll angle with the required calculation accuracy. To fulfil these objectives, an experimental environment was set up, composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model Band the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations highly approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risk situations, fulfilling real-time operation restrictions stated for this problem.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

“…First, the IoT-based sensor device developed in this study is based on Raspberry Pi which is small-size, low-cost, and powerful single-board computer device. Previous studies have shown significant advantages of utilizing Raspberry Pi such as for controlling and monitoring IoT system [ 83 ], estimating the roll angle of a vehicle using embedded neural network in real-time [ 84 ], hosting and serving the user interface of eHealth care system [ 59 ], and monitoring the temperature of lava lake using near infrared thermal camera [ 85 ]. Therefore, the proposed IoT-based sensor device developed in this study could be applied to monitor the manufacturing process in real-time.…”

Evaluation of Strategies for the Development of Efficient Code for Raspberry Pi Devices

“…Given its suitability for applications in many areas, the RPi has been used in a large number of research projects, from sensor networks [ 4 , 5 , 6 ], vehicle active safety systems [ 7 ], e-health care [ 8 ], big data analytics [ 9 ], and image analysis [ 10 ], to various other areas. It has also been widely used in educational projects [ 11 ], and in the construction of affordable and energy-efficient clusters consisting of up to 300 nodes [ 12 ], which partially offsets its lack of computing power.…”

Advanced Estimation Techniques for Vehicle System Dynamic State: A Survey

“…To predict the lateral load transfer and roll stability vector in roll stability control system, the NN-based roll angle estimations in [148,149,150] were taken into account using IoT low-cost devices and IMU where longitudinal and lateral accelerometer, yaw rate, and roll rate can be easily measured as training set, and the NN embedded in IoT low-cost devices can handle real-time constraints. Herein, the experimental result verified that the NN can obtain improved accuracy during the estimating process of vehicle roll angle with respect to KF [150].…”