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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.
Zio, Enrico,Pedroni, Nicola,Broggi, Matteo,Golea, Lucia Roxana Korean Nuclear Society 2009 Nuclear Engineering and Technology Vol.41 No.10
In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships.
ENRICO ZIO,NICOLA PEDRONI,MATTEO BROGGI,LUCIA ROXANA GOLEA 한국원자력학회 2009 Nuclear Engineering and Technology Vol.41 No.10
In this paper, an infinite impulse response locally recurrent neural network (IIR-LRNN) is employed for modelling the dynamics of the Lead Bismuth Eutectic eXperimental Accelerator Driven System (LBE-XADS). The network is trained by recursive back-propagation (RBP) and its ability in estimating transients is tested under various conditions. The results demonstrate the robustness of the locally recurrent scheme in the reconstruction of complex nonlinear dynamic relationships.
자동 주차를 위한 차량 조향 및 속도제어 알고리즘 개발
윤지환(Jihwan Yoon),이경수(Kyongsu Yi),Pietro Cerri,Albetro Broggi 한국자동차공학회 2012 한국자동차공학회 학술대회 및 전시회 Vol.2012 No.11
본 논문에서는 자동 주차를 위한 차량 제어 알고리즘을 다루고 있다. Global Positioning System(GPS) 정보로부터 차량의 위치와 heading angle, 주차 구역의 위치와 각도를 알 수 있다. 이를 기반으로 후진 시작 지점인 Middle Point를 결정하고, 현재 차량의 위치에서부터 Middle Point까지의 경로와 Middle Point에서부터 Goal Point까지의 경로를 현재 차량의 위치와 heading angle, 목적 지점의 위치와 각도를 구속조건으로 하여 3차 곡선으로 생성한다. 경로를 따르기 위한 조향각은 임계값을 넘지 않아야 한다. GPS 정보를 이용하여 주차 구역 근처의 지점으로 이동한다. 그리고 차량 후미에 Camera를 장착하여 후진 상황에서 주차구역을 인지하고 정확한 Goal Point와 Middle Point를 결정한다. 기어 변속을 위한 장치로서 Automatic Transmission Shifting Control (ATSC)을 사용하였다. 다양한 지점에서 주차가 가능한 경로를 생성하기 위해 시뮬레이션을 통해 검증하였고, 시험 차량을 통해 실제 차량에서의 안정적인 주차가 가능함을 검증하였다.
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>