Abstract
A method of estimating driving vehicle information usually uses a speed gun and a fixed speed camera. Estimating vehicle information using the speed gun has a high risk of traffic accidents by the operator and the fixed speed camera is not efficient in terms of installation cost and maintenance. The existing driving vehicle information estimation method can only measure each lane’s driving vehicle information, so it is impossible to measure multi-lanes simultaneously with a single measuring device. This study develops a distance measurement module that can acquire driving vehicle information in multi-lanes simultaneously with a single system using a drone. The distance measurement module is composed of two LiDAR sensors to detect the driving vehicle in one lane. The drone is located above the edge of the road and each LiDAR sensor emits the front/rear point of the road measuring point to detect the driving vehicle. The driving vehicle velocity is estimated by detecting the driving vehicle’s detection distance and transit time through radiation, with the drone LiDAR sensor placed at two measurement points on the road. The drone LiDAR sensor radiates two measuring points on the road and estimates the velocity based on driving vehicle’s detection distance and driving time. As an experiment, the velocity accuracy of the drone driving vehicle is compared with the speed gun measurement. The vehicle velocity RMSE for the first and second lanes using drones is 0.75 km/h and 1.3 km/h, respectively. The drone and the speed gun’s average error probabilities are 1.2% and 2.05% in the first and second lanes, respectively. The developed drone is more efficient than existing driving vehicle measurement equipment because it can acquire information on the driving vehicle in a dark environment and a person’s safety.
Highlights
This study develops the Vehicle detection module (VDM) and the DIAM to mount the drone as mission equipment
The driving vehicle was detected using the drone and the vehicle velocityConclusions was calculated based on the detection information
The driving vehicle velocity estimation was paper, calculated a distance theusing time by the the driving ve- velocity. In this the from driving vehicleand waspassing detected thedetecting drone and vehicle hiclecalculated with the drone at the two measurement points on thevelocity road
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Razvan et al [19] studied a method in which a vehicle detects objects, obstacles, pedestrians or traffic signs in foggy weather conditions by using Laser and LiDAR methods to estimate driving visibility. This method determines the vehicle operation method according to the vehicle and improves the autonomous vehicle’s stability. Shuang et al [25] studied the object matching framework based on affinefunction transformation for vehicle detection from the crewless aerial vehicle’s camera image This method effectively handles vehicles in various conditions such as scale change, direction change, shadow and partial occlusion.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
