Road Grade Estimation Using Crowd-Sourced Smartphone Data

Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps. Integration of road grade information will widen the scope of digital map's applications, which is primarily used for navigation, by enabling driving safety and efficiency applications such...

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Vydané v:2020 19th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) s. 313 - 324
Hlavní autori: Gupta, Abhishek, Hu, Shaohan, Zhong, Weida, Sadek, Adel, Su, Lu, Qiao, Chunming
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.04.2020
Predmet:
Accelerometers
Estimation
Gyroscopes
Human-centered computing→Ubiquitous and mobile computing systems and tools
Mobile computing
Roads
Robustness
Scalability
Sensors
Smartphones
Ubiquitous computing
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Popis
Shrnutí:Estimates of road grade/slope can add another dimension of information to existing 2D digital road maps. Integration of road grade information will widen the scope of digital map's applications, which is primarily used for navigation, by enabling driving safety and efficiency applications such as Advanced Driver Assistance Systems (ADAS), eco-driving, etc. The huge scale and dynamic nature of road networks make sensing road grade a challenging task. Traditional methods oftentimes suffer from limited scalability and update frequency, as well as poor sensing accuracy. To overcome these problems, we propose a cost-effective and scalable road grade estimation framework using sensor data from smartphones. Based on our understanding of the error characteristics of smartphone sensors, we intelligently combine data from accelerometer, gyroscope and vehicle speed data from OBD-II/smartphone's GPS to estimate road grade. To improve accuracy and robustness of the system, the estimations of road grade from multiple sources/vehicles are crowd-sourced to compensate for the effects of varying quality of sensor data from different sources. Extensive experimental evaluation on a test route of 9km demonstrates the superior performance of our proposed method, achieving 5× improvement on road grade estimation accuracy over baselines, with 90% of errors below 0.3 ° .
DOI:10.1109/IPSN48710.2020.00-25