Estimation of Vehicle Attitude, Acceleration, and Angular Velocity Using Convolutional Neural Network and Dual Extended Kalman Filter

Abstract

The acceleration of a vehicle is important information in vehicle states. The vehicle acceleration is measured by an inertial measurement unit (IMU). However, gravity affects the IMU when there is a transition in vehicle attitude; thus, the IMU produces an incorrect signal output. Therefore, vehicle attitude information is essential for obtaining correct acceleration information. This paper proposes a convolutional neural network (CNN) for attitude estimation. Using sequential data of a vehicle’s chassis sensor signal, the roll and pitch angles of a vehicle can be estimated without using a high-cost sensor such as a global positioning system or a six-dimensional IMU. This paper also proposes a dual-extended Kalman filter (DEKF), which can accurately estimate acceleration/angular velocity based on the estimated roll/pitch information. The proposed method is validated by real-car experiment data and CarSim, a vehicle simulator. It accurately estimates the attitude estimation with limited sensors, and the exact acceleration/angular velocity is estimated considering the roll and pitch angle with de-noising effect. In addition, the DEKF can improve the modeling accuracy and can estimate the roll and pitch rates.

Influence of Accelerometer Sensor Position for Measurement of Lateral Acceleration of Delivery Van for Cargo Securement

“…Here, the authors examine the use case of experimentally collected GNSS observations within an international GNSS service network. A methodology for using IGS observations for GNSS positioning performance studies is outlined [14,15].…”

Determination of Turning Radius and Lateral Acceleration of Vehicle by GNSS/INS Sensor

“…Ok et al [ 18 ] recognized using sensors applied for positioning data or a six-dimensional inertial measurement unit (IMU) as originating too high cost intensity, therefore the authors developed a convolutional neural network (CNN) together with a dual-extended Kalman filter to estimate attitude of a vehicle. Their research were actual data applied in simulation modelling.…”

“…The above-discussed literature references can be listed into following groups: Research focused on dynamics of passenger vehicles (including automobiles, vans, coach buses, etc. ): Zhou et al [ 6 ], Xu et al [ 7 ], Hamersma and Els [ 8 ], Shu et al [ 9 ], Jurecki and Stańczyk [ 11 ], Sazgar et al [ 17 ], Visar et al [ 12 ], Tian et al [ 14 ], Ok et al [ 18 ], Vu [ 46 ], Dižo et al [ 55 ]—most of the mentioned research applied simulation methods, on the other hand, real conditions were included in Jurecki and Stańczyk [ 11 ], Xu et al [ 13 ], Zamfir et al [ 19 ]. Research focused on dynamics of freight transport vehicles: Shojaei et al [ 20 ], Wang and He [ 21 ], Braghin et al [ 23 ], Lewington [ 24 ], Balson et al [ 25 ], Zhang et al [ 26 ], Qu et al [ 27 ], Ikhsan et al [ 28 ], Senalik and Medanic [ 29 ], Ibrahim and Singh [ 30 ], Winkler et al [ 31 ], Romero et al [ 32 ], Jiang et al [ 33 ], Cao et al [ 34 ], Tavassoli Kallebasti et al [ 35 ], Huang et al [ 36 ], Mischinger et al [ 37 ], Marienka et al [ 49 ]—most of the references listed before the hyphen applied simulation and numerical methods; however, results in field exploitation were presented, e.g., in Winkler et al [ 31 ], Mischinger et al [ 37 ], Skrúcaný et al [ 48 ], Skrúcaný et al [ 47 ], Vlkovský et al [ 52 ], Vlkovský et al [ …”

“…Research focused on dynamics of passenger vehicles (including automobiles, vans, coach buses, etc. ): Zhou et al [ 6 ], Xu et al [ 7 ], Hamersma and Els [ 8 ], Shu et al [ 9 ], Jurecki and Stańczyk [ 11 ], Sazgar et al [ 17 ], Visar et al [ 12 ], Tian et al [ 14 ], Ok et al [ 18 ], Vu [ 46 ], Dižo et al [ 55 ]—most of the mentioned research applied simulation methods, on the other hand, real conditions were included in Jurecki and Stańczyk [ 11 ], Xu et al [ 13 ], Zamfir et al [ 19 ].…”

“…The list of publications presented at the end of Section 1.2 , which grouped the references [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 ...…”