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Development of Advanced Obstacle-Detecting Methods for Level Crossings Hiroto Takeuchi, Tetsuyoshi Shozawa Central Japan Railway Company, Aichi, Japan Abstract In railways, level crossings are safety-critical points because of the risk of collisions between motorcars and trains. Therefore, major automated level crossings in Japan are equipped with obstacle-detecting systems in order to prevent fatal accidents. When these systems detect obstacles in level crossings, they control the related signals and stop the approaching train. Most of the obstacle-detecting systems in Japan are the light-interrupting type, which detects the obstacle that interrupts the light beam travelling from an optical transmitter to the corresponding receiver. However, this system often becomes unusable when it snows heavily in the winter. Daily maintenance of the lenses to remove stains is another problem. It has recently become essential to reduce the frequency of inspection and adjustment, and the time needed for installation and replacement due to short train headways under the high transportation density. Moreover, pedestrians detected by sensors cause unneeded train stops and serious train disturbance occurs. In this paper, three types of advanced obstacle-detecting methods, which are the millimetre-wave- based system, laser range scanners and geomagnetic sensors, are reviewed and the test results of detection performance, weather tolerance, maintenability and effectiveness of preventing train disturbance caused by pedestrians are reported. It is shown that these methods are promising from the viewpoints of preventing accidents, reducing operational disturbance and decreasing installation and maintenance costs. Introduction In railways, level crossings are safety-critical points because of the risk of collisions between motorcars and trains. Therefore, major automated level crossings in Japan are equipped with obstacle-detecting systems in order to prevent fatal accidents. When these systems detect obstacles in level crossings, they control the related signals and stop the approaching train. There exist about 2000 level crossings and 700 obstacle-detecting systems in Central Japan Railway Company (CJRC). Most of the obstacle-detecting systems are the light-interrupting type, which detects the obstacle that interrupts the light beam travelling from an optical transmitter to the corresponding receiver. However, this system often becomes unusable when it snows heavily in the winter. Daily maintenance of the lenses to remove stains is another problem. It has recently become essential to reduce the frequency of inspection and adjustment, and the time needed for installation and replacement due to short train headways under high transportation density. Moreover, pedestrians detected by sensors cause unneeded train stops and serious train disturbance occurs. Therefore, three types of advanced obstacle-detecting methods have been studied by CJRC for mass replacement of decrepit equipment. Existing Obstacle-Detecting Methods When the obstacle-detecting system detects objects, it stops the approaching trains by controlling signals. The existing obstacle-detection systems are categorised into three types, which are the light-interrupting type, the induction loop type and the ultrasonic type. The light-interrupting type, which is the most widely used in Japanese railways, detects the obstacle when it interrupts the light beam travelling from an optical transmitter to the corresponding receiver as shown in Fig. 1. Although the other two types of obstacle-detecting systems are relatively tolerant against bad weather and environmental conditions, it is difficult to install or maintain them, because they need cables under the ground or the ultrasonic transducers over the level crossings. Therefore, the applications of these two types are restricted.

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Page 1: Development of Advanced Obstacle-Detecting Methods for ... · When the obstacle-detecting system detects objects, it stops the approaching trains by controlling signals. The existing

Development of Advanced Obstacle-Detecting Methods for Level Crossings

Hiroto Takeuchi, Tetsuyoshi Shozawa

Central Japan Railway Company, Aichi, Japan

Abstract

In railways, level crossings are safety-critical points because of the risk of collisions between motorcars and trains. Therefore, major automated level crossings in Japan are equipped with obstacle-detecting systems in order to prevent fatal accidents. When these systems detect obstacles in level crossings, they control the related signals and stop the approaching train. Most of the obstacle-detecting systems in Japan are the light-interrupting type, which detects the obstacle that interrupts the light beam travelling from an optical transmitter to the corresponding receiver. However, this system often becomes unusable when it snows heavily in the winter. Daily maintenance of the lenses to remove stains is another problem. It has recently become essential to reduce the frequency of inspection and adjustment, and the time needed for installation and replacement due to short train headways under the high transportation density. Moreover, pedestrians detected by sensors cause unneeded train stops and serious train disturbance occurs. In this paper, three types of advanced obstacle-detecting methods, which are the millimetre-wave-based system, laser range scanners and geomagnetic sensors, are reviewed and the test results of detection performance, weather tolerance, maintenability and effectiveness of preventing train disturbance caused by pedestrians are reported. It is shown that these methods are promising from the viewpoints of preventing accidents, reducing operational disturbance and decreasing installation and maintenance costs.

Introduction

In railways, level crossings are safety-critical points because of the risk of collisions between motorcars and trains. Therefore, major automated level crossings in Japan are equipped with obstacle-detecting systems in order to prevent fatal accidents. When these systems detect obstacles in level crossings, they control the related signals and stop the approaching train. There exist about 2000 level crossings and 700 obstacle-detecting systems in Central Japan Railway Company (CJRC). Most of the obstacle-detecting systems are the light-interrupting type, which detects the obstacle that interrupts the light beam travelling from an optical transmitter to the corresponding receiver. However, this system often becomes unusable when it snows heavily in the winter. Daily maintenance of the lenses to remove stains is another problem. It has recently become essential to reduce the frequency of inspection and adjustment, and the time needed for installation and replacement due to short train headways under high transportation density. Moreover, pedestrians detected by sensors cause unneeded train stops and serious train disturbance occurs. Therefore, three types of advanced obstacle-detecting methods have been studied by CJRC for mass replacement of decrepit equipment.

Existing Obstacle-Detecting Methods

When the obstacle-detecting system detects objects, it stops the approaching trains by controlling signals. The existing obstacle-detection systems are categorised into three types, which are the light-interrupting type, the induction loop type and the ultrasonic type. The light-interrupting type, which is the most widely used in Japanese railways, detects the obstacle when it interrupts the light beam travelling from an optical transmitter to the corresponding receiver as shown in Fig. 1. Although the other two types of obstacle-detecting systems are relatively tolerant against bad weather and environmental conditions, it is difficult to install or maintain them, because they need cables under the ground or the ultrasonic transducers over the level crossings. Therefore, the applications of these two types are restricted.

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Figure 1: Existing obstacle-detecting system (light-interrupting type).

Millimetre-wave-based System

In order to improve the weather tolerance, the millimetre-wave-based system has been developed [1]. The obstacle is detected when the millimetre-wave (76.0 – 77.0 GHz) reflected from the obstacle is received or the refector, which does not need wiring, becomes invisible by using the antenna. Fig. 3 shows the outlook of millimetre-wave antennas and reflectors. The range is from 2 m to 40 m. In order to avoid the interference with other ladars such as anti-collision systems on motorcars, a new modulation (FM-CW) has been designed for the exclusive use of railways. This system meets the international safety guide ICNIRP and its power density is limited below 7.97 W/m2 rms. The interference between the antennas, which are used for obstacle detection at an identical level crossing, is prevented by achiving a sharp electromagnetic field of each antenna. Consequently, the optical transmitters and the corresponding receivers in the conventional light-interrupting type in Fig. 1 can be replaced by the millimetre-wave antennas and reflectors as shown Fig. 2. The test results have shown that an equivalent obstacle-detecting accuracy is achieved by the millimetre-wave-based system. Because this millimetre-wave-based sysyem does not need the unobstructed optical view, which is essential to the conventional light-interrupting type, it has exhibited a good weather tolerance in the snow scattered by passing trains at level crossings as shown in Fig. 4.

Figure 2: Millimetre-wave-based system

(millimetre -wave-interrupting type).

Receiver

Transmitter

Obstacle

Road

Railway

Reflector

Obstacle

Distance is measured

Antenna

Bars

Cable

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Figure 3: Millimetre-wave-based Figure 4: Test site in the snow antennas (left) and reflectors (right). scattered by passing trains.

Laser Range Scanner with Automatic Pedestrian Tracking Algorithm

For detecting small objects video-based solutions exhibit some disadvantages, such as narrow field of view, coverage limitation of setting, and susceptibility to change in light conditions. Therefore, multiple laser scanners are investigated [4]. Single-row laser range scanners with high scanning ratio, wide viewing angle and long-range measurement capability are used. They are set on the ground so that horizontal cross sections of the surroundings, containing moving feet of pedestrians as well as still objects, are obtained in a rectangular coordinate system of real dimension. The distance between the scanner and an object is calculated based on the measured time-of-flight of the pulse laser. Integration of multiple laser scanners, which is performed by using Hermart transformation that deals with shift and rotation, has the advantage of reducing the effect of occlusion. Since an interface for manual setting up is implemented in the software, a number of laser scanners can easily be calibrated. Fig.5 shows a sample laser scan, where laser points are coloured through background subtraction. The green image is the still object and the moving motorcar, bicycles and pedestrians are white. Background images are initially generated by each client computer, and sent to the server computer for two purposes. Firstly, they are registered to find transformation matrixes from each sensor’s local coordinate system to a pre-defined global one. Secondly, they are shown as well as the data of moving feet that are recorded in each scan for real-time monitoring.

Figure 5: Detected objects by using one laser range scanner.

Scanner 1 Scanner 2

Scanner 3 Scanner 4

Motorcar

0 Bicycle

Pedestrian

Background information

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Since multiple laser beams hit an identical object because of high angle-resolution, laser points are clustered based on the relative distances and specific shapes of moving objects. A simplified pedestrian’s walking model based on the typical appearance of moving feet is defined. This model consists of three kinds of state parameters, position, speed, and acceleration. Position and speed vectors of each pedestrian change continuously, while acceleration parameters change by swing phase in a discontinuous way. Since there might be many points shooting upon the same foot, a process is first conducted clustering the moving points of the integrated range frame that have a radius less than a normal foot (e.g. 15cm). The central points of these clusters are treated as foot candidates. Trajectory tracking is conducted by first extending the trajectories that have been extracted in previous frames, then looking for the seeds of new trajectories from the foot candidates that are not associated to any existing trajectories (Fig. 6). A tracking algorithm utilising a discrete Kalman filter has been developed extending the existing trajectories to the current range frame. Figure 6: Automatic pedestrian tracking. By recognising the difference between large objects (like cars and trucks) and small objects like pedestrians, it becomes possible to stop trains immediately only for large objects and avoid unnecessary emergency stops of trains. For small objects which are moving over a specific speed (e.g. pedestrian’s normal walking speed), an announcement or warning of security assurance for pedestrians in the closed level crossings could be given. On the other hand, trains shall be stopped immediately when the small object moves under a specific speed or automatic pedestrian’s tracking is failed. Fig. 7 shows the trajectories of pedestrians who are automatically recognised and traced by using one laser range scanner.

Figure 7: Automatic pedestrian tracking by using one laser range scanner.

Clustering Laser point

Two feet One pedestrian

Grouping

Case 1

f1 f2 f3 f1 f2 f3

Clustering

Grouping

Tracking

Case 2

Foot of pedestrian

Laser range scanner

Background information

Trajectories of pedestrians

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When object s are continuously measured at the same location, these still objects are defined as background. Even when a laser scanner cannot detect invisible objects by point data appearance because of their low reflectance ratio, they can be detected by using the background occlusion data [5] . By calculating the distance subtraction between range images and the background image, the part of hidden background is defined and the position of the moving object can be estimated. This robust detecting method, with background occlusion information and without point data appearance, has been examined at a busy level crossing. The intersection points of extended lines with visual hull restore the outline of an object. The position-estimating process for a motorcar by connecting the cast shadow (hidden background) and the corresponding scanner position is demonstrated in Fig. 8. It has been shown that each intersection (visual hull) defines the position of the moving object (red area in Fig. 8) and the invisible object can be detected without dependence on the point data appearance.

Figure 8: Estimation of motorcar position by background occlusion.

Geomagnetic Sensor

For traffic surveillance, geomagnetic sensors have been developed and high accuracy has been achieved. Geomagnetic sensors, which measure the absolute values of the geomagnetic field in three dimensions, have a distinguished feature of easy installation and maintenance [2] . The configuration of the obstacle-detecting system with geomagnetic sensors is shown in Fig. 9 . When iron obstacles like motorcars are stuck in the closed level crossing, they cause geomagnetic field change. The measurement depends only on the geomagnetic field and is not affected by the weather. Since the sensors can be calibrated remotely, the train disturbance caused by maintenance and inspection is minimised. In order to realise reliable object-detecting systems, the magnetic field change caused by traction current should be considered. This field change can be cancelled by subtracting the simultaneously measured values by two geomagnetic sensors, which are fixed along the rail. Fig.10 shows the magnetic filed change caused by a motorcar on rails. Each curve represents the field change in X-, Y- or Z-axis and the resultant of the field change. The test results have shown the satisfactory detection performance.

Cast shadow (hidden background)

Scanner 1 Scanner 2

Scanner 4

Background Information

Scanner 3

Visual Hull

Four scanners are co-ordinated.

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-0.6-0.4-0.2

00.20.40.60.8

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Measuring point

X-axisY-axisZ-axisResultant

AU Motorcar

Figure 9: Configuration of obstacle-detecting system Figure 10: Geomagnetic field change caused

with geomagnetic sensors. by a motorcar on rails.

Conclusion

Three types of advanced obstacle-detecting methods, which have been developed by CJRC, have been reviewed and discussed. The first is the millimetre-wave-based system, which detects the reflected millimetre-waves from obstacles. This system has exhibited a good maintainability and weather tolerance against the snow which is scattered by passing trains. The second is the laser range scanners, which are capable of detecting objects smaller than motorcars utilising both laser range scanner technologies and digital signal processing techniques. A simple pedestrian’s walking model based on the typical appearance of moving feet has been defined and a tracking method utilising a Kalman filter has been developed. The performances of both the robust obstacle detection with background occlusion and pedestrian-tracking algorithm have successfully been verified. The third is the geomagnetic sensor, which can detect iron objects by measuring the change of the geomagnetic field. Precision response performance has been verified through field tests. Consequently, it has been shown that these methods are promising from the viewpoints of preventing accidents, reducing operational disturbance and decreasing installation and maintenance costs.

References

[1] T. Shozawa, H. Takeuchi, A. Asano, H. Nagasawa. “Development of millimetre-wave-based obstacle-detecting system for level crossings”, Proceedings of the J-RAIL2005 symposium, pp. 307-310, (2005). (in Japanese)

[2] T. Shozawa, H. Takeuchi, Y. Ikeda. “Object detection in railways by sensing geomagnetic field”, Proceedings of the International Conference on Electrical Engineering (ICEE), PS2-60 (F0073), (2005).

[3] D. Streller, K. Dietmayer. “Object tracking and classification using a multiple hypothesis approach”, Proceedings of the IEEE Intelligent Vehicle Symposium (IVS), pp. 808-812, (2004).

[4] H. Takeuchi, T. Shozawa, H. Miki, R. Shibasaki, H. Zhao, K. Nakamura. “A study on obstacle detection with automatic pedestrian tracking at railway level crossings by using laser range scanners”, IEEJ Industry Applications Society, Vol. 125-D, pp. 321-328, (2005). (in Japanese)

[5] H. Takeuchi, T. Shozawa, R. Shibasaki, H. Zhao, K. Nakamura, K. Iwata. “Obstacle detection with automatic pedestrian tracking at level crossings using multiple single-row laser range scanners”, Proceedings of the international conference ASPECT2006, No. 8, (2006).

Level crossing barriersMagnetic sensor

Detecting area

Cable

Rail