Next Article in Journal
Average Consensus over Mobile Wireless Sensor Networks: Weight Matrix Guaranteeing Convergence without Reconfiguration of Edge Weights
Previous Article in Journal
Recent Advances in the Electrochemical Sensing of Venlafaxine: An Antidepressant Drug and Environmental Contaminant
Open AccessArticle

Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle

1
School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China
2
School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(13), 3676; https://doi.org/10.3390/s20133676
Received: 24 May 2020 / Revised: 23 June 2020 / Accepted: 28 June 2020 / Published: 30 June 2020
(This article belongs to the Section Intelligent Sensors)
Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness. View Full-Text
Keywords: autonomous vehicle; laser measurement; model predictive control; measurement uncertainty autonomous vehicle; laser measurement; model predictive control; measurement uncertainty
Show Figures

Figure 1

MDPI and ACS Style

Ni, T.; Li, W.; Zhao, D.; Kong, Z. Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle. Sensors 2020, 20, 3676.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop