OBD-II Sensor Approaches for The IMU and GPS Based Apron Vehicle Positioning System

Suwandi, Bondan; Pinastiko, Widrianto Sih; Roestam, Rusdianto

doi:10.1109/icsecc.2019.8907036

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…In one study, data from the sensors is gathered, and then a fusion approach is used to assess the outcomes. In the experiment with a straight track condition, several data of positioning, such as velocity and course, represent the positioning data, while the vehicle velocity and vehicle course also represent in a closed loop track condition [19]. According to this study, an additional OBD-II sensor in a system, in addition to a single IMU and GPS positioning, can enhance and provide superior performance.…”

Section: Literature Reviewmentioning

confidence: 97%

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Rasheed,

Qazi,

Dur e Shawar Agha

et al. 2024

VFAST trans. softw. eng.

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The automobile industry has a growing need for reliable and safe health monitoring systems equipped with low-cost sensor networks and intelligent algorithms. This paper provides an overview of approaches already exist, used in on-board health monitoring systems for vehicles. It focuses on the methodologies, theories, and applications employed in the data measurement and data analysis systems of vehicle (cars) on-board health monitoring systems. A fault detection and diagnosis system, which is accurate, plays a vital role in ensuring the safety of autonomous vehicles by preventing potentially dangerous situations. This study focuses on emphasizing a fault diagnosis system that utilizes hybrid methods. Among the various options considered in this analysis, internal sensors emerge as the preferred choice due to their numerous benefits, including affordability, durability, widespread availability, ease of access, and low energy consumption. Model-based methods require various techniques that may introduce errors to estimation results, while signal-based methods necessitate a time-consuming process of including all possible conditions in a pre-built database. Based on this review, future development trends in designing new low-cost health monitoring systems for vehicles are also discussed.

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Section: Literature Reviewmentioning

confidence: 97%

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Rasheed,

Qazi,

Dur e Shawar Agha

et al. 2024

VFAST trans. softw. eng.

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show abstract

“…Previous studies on urban positioning can be classified into three categories: multi-constellation GNSS, integrated GNSS and inertial navigation system (INS) and multi-sensor fusion augmenting GNSS/INS with other aiding sensors. As aiding sensors to GNSS/INS, various sensors such as magnetic compass, precise electronic map, vision sensor, LiDAR, altimeter, odometer and velocity sensor have been considered [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Kalman–Hatch dual‐filter integrating global navigation satellite system/inertial navigation system/on‐board diagnostics/altimeter for precise positioning in urban canyons

Kim

Lee

2021

IET Radar Sonar & Navi

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For improved positioning in urban canyons, this study proposes an efficient dual‐filter method integrating multi‐constellation global navigation satellite system (GNSS), an inertial navigation system (INS), a barometric altimeter and an on‐board diagnostics (OBD) module. The proposed method consists of a position‐domain (PD) Hatch filter and velocity Kalman filter. The Hatch filter is operated as the main positioning filter and the Kalman filter is operated as a sub‐filter to aid the main filter for the occasional GNSS outages. The Hatch filter combines barometric altimeter measurements with GNSS pseudorange and carrier phase measurements. The Kalman filter integrates the inertial measurement unit (IMU) measurements with GNSS Doppler shift and OBD speed measurements. A semi‐simulation method is applied for the accurate performance evaluation of the proposed method compared with the several conventional methods under deficient satellite visibility, multipath errors and cycle slips caused by urban canyons. By the test results, it is demonstrated that the proposed method can bound the positioning errors effectively by utilising low‐cost all‐weather sensors. The RMSEs of the proposed Kalman–Hatch dual‐filter algorithm were shown as 0.05, 0.19 and 0.11 m, respectively, and the maximum errors were shown as 0.16, 0.26 and 0.40 m, respectively, in the east, north and upward directions.

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Multiple Object Tracking using Radar and Vision Sensor Fusion for Autonomous Vehicle

Kurapati¹,

Suma²,

Chavan³

2020

2020 IEEE International Conference for Innovation in Technology (INOCON)

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OBD-II Sensor Approaches for The IMU and GPS Based Apron Vehicle Positioning System

Cited by 3 publications

References 4 publications

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Machine Learning Approaches for In-Vehicle Failure Prognosis in Automobiles: A Review

Kalman–Hatch dual‐filter integrating global navigation satellite system/inertial navigation system/on‐board diagnostics/altimeter for precise positioning in urban canyons

Multiple Object Tracking using Radar and Vision Sensor Fusion for Autonomous Vehicle

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