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RISS 인기검색어
- 검색키워드
- 저자 : A. BROGGI (검색결과 2 건)
- 무료
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HIGH PERFORMANCE MULTI-TRACK RECORDING SYSTEM FOR AUTOMOTIVE APPLICATIONS
A. BROGGI,S. DEBATTISTI,M. PANCIROLI,P. GRISLERI,E. CARDARELLI,M. BUZZONI,P. VERSARI 한국자동차공학회 2012 International journal of automotive technology Vol.13 No.1
This paper describes a framework used to develop and run automotive applications both on board of vehicles and in laboratory. It includes the recording system designed and implemented in the GOLD framework. The system can record data from different sensors, such as cameras, laserscanners, radars, GPS, IMU, IO boards. The system can easily be expanded adding new device drivers. An in-RAM prerecording functionality is available to let the user record events started in the past. An index file collects essential information on each recorded event, such as timestamp, source identifier, and other sourcespecific data. Different file formats can be used to store data on disks; standard file formats are available for images and audio, small data such as CAN messages or GPS data are recorded directly into the index file. In order to have a faster lookup of a particular scene, the system is also equipped with a user interface that allows to insert tags during the recording. This system has been under development and successfully employed in the last 15 years to acquiring data for several VisLab projects. The description of two case studies is included in this paper. BRAiVE is an advanced prototype used as mobile laboratory to acquire data for different purposes. VIAC is a trip from Parma, Italy, to Shanghai, China, performed to test the robustness of VisLab driving assistance systems; the autonomous driving sessions have been recorded generating a unique database suitable to study and possibly improve the algorithm performance.
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Environment-Detection-and-Mapping Algorithm for Autonomous Driving in Rural or Off-Road Environment
Jaewoong Choi,Junyoung Lee,Dongwook Kim,Soprani, G.,Cerri, P.,Broggi, A.,Kyongsu Yi IEEE 2012 IEEE transactions on intelligent transportation sy Vol.13 No.2
<P>This paper presents an environment-detection-and-mapping algorithm for autonomous driving that is provided in real time and for both rural and off-road environments. Environment-detection-and-mapping algorithms have been designed to consist of two parts: (1) lane, pedestrian-crossing, and speed-bump detection algorithms using cameras and (2) obstacle detection algorithm using LIDARs. The lane detection algorithm returns lane positions using one camera and the vision module “VisLab Embedded Lane Detector (VELD),” and the pedestrian-crossing and speed-bump detection algorithms return the position of pedestrian crossings and speed bumps. The obstacle detection algorithm organizes data from LIDARs and generates a local obstacle position map. The designed algorithms have been implemented on a passenger car using six LIDARs, three cameras, and real-time devices, including personal computers (PCs). Vehicle tests have been conducted, and test results have shown that the vehicle can reach the desired goal with the proposed algorithm.</P>
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