3,785 research outputs found
Modelling shared space users via rule-based social force model
The promotion of space sharing in order to raise the quality of community living and safety of street surroundings is increasingly accepted feature of modern urban design. In this context, the development of a shared space simulation tool is essential in helping determine whether particular shared space schemes are suitable alternatives to traditional street layouts. A simulation tool that enables urban designers to visualise pedestrians and cars trajectories, extract flow and density relation in a new shared space design and achieve solutions for optimal design features before implementation. This paper presents a three-layered microscopic mathematical model which is capable of representing the behaviour of pedestrians and vehicles in shared space layouts and it is implemented in a traffic simulation tool. The top layer calculates route maps based on static obstacles in the environment. It plans the shortest path towards agents' respective destinations by generating one or more intermediate targets. In the second layer, the Social Force Model (SFM) is modified and extended for mixed traffic to produce feasible trajectories. Since vehicle movements are not as flexible as pedestrian movements, velocity angle constraints are included for vehicles. The conflicts described in the third layer are resolved by rule-based constraints for shared space users. An optimisation algorithm is applied to determine the interaction parameters of the force-based model for shared space users using empirical data. This new three-layer microscopic model can be used to simulate shared space environments and assess, for example, new street designs
A machine learning approach to pedestrian detection for autonomous vehicles using High-Definition 3D Range Data
This article describes an automated sensor-based system to detect pedestrians in an autonomous vehicle application. Although the vehicle is equipped with a broad set of sensors, the article focuses on the processing of the information generated by a Velodyne HDL-64E LIDAR sensor. The cloud of points generated by the sensor (more than 1 million points per revolution) is processed to detect pedestrians, by selecting cubic shapes and applying machine vision and machine learning algorithms to the XY, XZ, and YZ projections of the points contained in the cube. The work relates an exhaustive analysis of the performance of three different machine learning algorithms: k-Nearest Neighbours (kNN), NaĂŻve Bayes classifier (NBC), and Support Vector Machine (SVM). These algorithms have been trained with 1931 samples. The final performance of the method, measured a real traffic scenery, which contained 16 pedestrians and 469 samples of non-pedestrians, shows sensitivity (81.2%), accuracy (96.2%) and specificity (96.8%).This work was partially supported by ViSelTR (ref. TIN2012-39279) and cDrone (ref. TIN2013-45920-R) projects of the Spanish Government, and the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15). 3D LIDAR has been funded by UPCA13-3E-1929 infrastructure projects of the Spanish Government. Diego Alonso wishes to thank the Spanish Ministerio de EducaciĂłn, Cultura y Deporte, Subprograma Estatal de Movilidad, Plan Estatal de InvestigaciĂłn CientĂfica y TĂ©cnica y de InnovaciĂłn 2013–2016 for grant CAS14/00238
TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China
TiEV is an autonomous driving platform implemented by Tongji University of
China. The vehicle is drive-by-wire and is fully powered by electricity. We
devised the software system of TiEV from scratch, which is capable of driving
the vehicle autonomously in urban paths as well as on fast express roads. We
describe our whole system, especially novel modules of probabilistic perception
fusion, incremental mapping, the 1st and the 2nd planning and the overall
safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future
Challenge of China held at Changshu. We show our experiences on the development
of autonomous vehicles and future trends
Video vehicle detection at signalised junctions: a simulation-based study
Many existing advanced methods of traffic signal control depend on information about
approaching traffic provided by inductive loop detectors at particular points in the road. But
analysis of images from CCTV cameras can in principle provide more comprehensive
information about traffic approaching and passing through junctions, and cameras may be
easier to install and maintain than loop detectors, and some systems based on video detection
have already been in use for some time.
Against this background, computer simulation has been used to explore the potential of
existing and immediately foreseeable capability in automatic on-line image analysis to extract
information relevant to signal control from images provided by cameras mounted in
acceptable positions at signal-controlled junctions. Some consequences of extracting relevant
information in different ways were investigated in the context of an existing detailed
simulation model of vehicular traffic moving through junctions under traffic-responsive signal
control, and the development of one basic and one advanced algorithm for traffic-responsive
control. The work was confined as a first step to operation of one very simple signalcontrolled
junction.
Two techniques for extraction of information from images were modelled - a more ambitious
technique based on distinguishing most of the individual vehicles visible to the camera, and a
more modest technique requiring only that the presence of vehicles in any part of the image
be distinguished from the background scene. In the latter case, statistical modelling was used
to estimate the number of vehicles corresponding to any single area of the image that
represents vehicles rather than background.
At the simple modelled junction, each technique of extraction enabled each of the algorithms
for traffic-responsive control of the signals to achieve average delays per vehicle appreciably
lower than those given by System D control, and possibly competitive with those that MOVA
would give, but comparison with MOVA was beyond the scope of the initial study.
These results of simulation indicate that image analysis of CCTV pictures should be able to
provide sufficient information in practice for traffic-responsive control that is competitive
with existing techniques. Ways in which the work could be taken further were discussed with
practitioners, but have not yet been progressed
Learning Behavioural Context
The original publication is available at www.springerlink.co
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