comparison of two versions of blind spot information systems in bytel

PROCEEDINGS OF THE INSTITUTE OF VEHICLES 3(99)/2014
Wojciech Skarka1, Katarzyna Jezierska-Krupa2, Maciej Krysiak3
COMPARISON OF TWO VERSIONS OF BLIND SPOT INFORMATION
SYSTEMS IN BYTEL AND MUSHELLKA TWO RACE VEHICLES
1. Introduction
Since 2012 the Smart Power Team [12] has been actively participating in worldwide
competition – The Shell Eco-marathon (SEM). The main idea behind this event is to
gather and encourage students from all over the world to invent and build the most
energy efficient vehicle possible. This year SEM edition will be our third start in
Prototype category, but also, hopefully successful, debut in Urban category.
Our first child – MuSHELLka [7] (Fig. 1), is a prototype vehicle, the second one –
Bytel, is an urban car.
Fig. 1. Vehicle MuSHELLka on Shell Eco-marathon 2013 in Rotterdam Netherland
Since the beginning our team has put a lot of pressure on the safety issues [8, 9].
MuSHELLka is equipped with such safety systems as: BLIS [1, 2, 3] (Blind Spot
Information System) and CDIS (Collision Detection and Information System).
Especially for Bytel not only have we improved previously mentioned systems but also
we have developed new Active Driver Assistance Systems: OHRS (Overtaking Horn
Reminder System), DPMS (Dew Point Measurement System) and MSS (Main
Supervision System).
In this article we have focused on comparison of Blind Spot Information System that
is already implemented in MuSHELLka and, for now just designed, BLIS for Bytel.
1
Prof. Wojciech Skarka, Institute of Fundamentals of Machinery Design, Silesian University of Technology
Eng. Katarzyna Jezierska-Krupa, Institute of Fundamentals of Machinery Design, Silesian University
of Technology
3
Eng. Maciej Krysiak, Institute of Fundamentals of Machinery Design, Silesian University of Technology
2
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Fig. 2. Methodology of verification and virtual prototyping of automation systems
on the basis of MuSHELLka race vehicle
2. BLIS sytem for MuSHELLka and Bytel
BLIS, used in MuSHELLKA vehicle, was a prototype system which was elaborated
for researching the possibilities of using it in a race vehicle and therefore, inexpensive
versions of sensors were used. The system research with the aid of virtual prototyping
environment based on tailor made methodology [5, 6] for this example, confirmed the
purposefulness of using this system and its potentials (Fig. 2). The consecutive research
carried on the race track and test loop in Gliwice endorsed the features of the system
and allowed its validation [6].
Fig. 3. Model of urban category Bytel vehicle
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The subsequent research is much easier due to the elaboration of virtual environment
of the race spot i.e. the racing track in Rotterdam.
The necessity to implement improvements in BLIS was triggered by experiences, we
had opportunity to gather, and also by new technologies we have come to learn.
BLIS system in a new Bytel vehicle (Fig. 3) is tested in the environment of virtual
prototyping, which was elaborated in Prescan system [11] with Matlab/Simulink and it
consists of the model of race track, the vehicle, the model of automation system of the
set with the models of used sensors.
3. Comparision of two versions of BLIS system
The main difference in these two systems is the type of sensor used – in
MuSHELLka’s BLIS we used two sonars and one fotoelectric sensor, while in Bytel we
plan to use one Hokuyo lidar. This way we want to not only improve the range of our
measurements but also the system’s accuracy.
At the design stage of a new version of BLIS expected use of photoelectric sensors
(Fig. 4a), Microsoft Kinect devices (Fig. 4b) and Hokuyo lidar (Fig. 4c).
Fig 4. Concepts of BLIS system for Urban type vehicle
Optimal solution, due to the range, accuracy and compact design is the use of the
Hokuyo lidar. It is characterized by an angular range of 270 degrees and the detectability
of objects up to 50 meters. Figure 4 shows a graphical comparison of the scope of the
systems. For a system based on Hokuyo lidar showing the scan area with a radius of 4m
would not obscure figure. Scan angle is limited by the rear of the vehicle (Fig. 5).
BLIS system in the vehicle MuSHELLka (Fig. 6)has a smaller scan angle despite the
use of a larger number of sensors. Due to the amount of data provided by Hokuyo Lidar
is required a PC. Data processing using a single board computer Kontron PiTX. Displays
information for the driver will be show via Tablet. Tablet offers a high screen resolution
in a small screen. The use of a computer requires a greater demand for energy. Hokuyo
Lidar itself requires energy needs of about 5W, single board computer with a hard disk
drive requires 30W of power. Compared to the system applied MuSHELLka, the energy
demand increases 2.5 times.
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Fig 5. Comparision of two versions of BLIS system
Fig. 6.Location of BLIS system sensors in Mushelka: 1 - Ultrasonic sensors HC-SR04;
2 - photoelectric sensor Datalog S300
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Table 1. BLIS versions summary
Mushellka
Bytel
Sensors
2 ultrasonic,
1 fotoeletric
1 laser range scanner
Range of activity
Range: 2-3,5m,
Angle: 160°
Range: 4-50m, Angle: 190°
Signal analysis device
Arduino Due
One board micro computer
Informing the driver
diodes
Touchscreen
System cost
About 600 zł
Depending on the scanner
model: 7000 -24 000 zł
4. Conclusions
The proposed support safety system is much more expensive than the previous
version (Tab. 1), but such a system increases the accuracy of vehicle detection. The use
Hokuyo lidar allows for further development of the system towards the creation of an
autonomous vehicle. Based on data from the signal of an autonomous vehicle can
determine whether it can change lanes. Such a sensor can also be used for automatic
parking. When creating a new version of BLIS thought about using cameras and image
recognition, but this would require massive computing power to analyze real-time image
in such a dynamic environment.
References
[1]
Gietelink O.J., Ploeg J., De Schutter B., (2009) “Development of a driver
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[2]
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[3]
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Web page - Prescan, Tass (2014) www.tass-safe.com/en/products/prescan
Web page - Modeling of Design Students Association (2014) www.mkm.polsl.pl
Abstract
Since 2012 the Smart Power Team has been actively participating in worldwide
competition – The Shell Eco-marathon. From the beginning, the team is working to
increase driver safety on the road. This article focuses on the comparison of systems to
detect objects in the blind spot of the vehicle used in Mushellka and the proposed Bytel
vehicles.
Key words: BLIS, driver safety, Shell Eco-marathon
PORÓWNANIE DWÓCH WERSJI SYSTEMU DO WYKRYWANIA
OBIEKTÓW W MARTWYM POLU W LUSTERKACH W POJAZDACH
WYŚCIGOWYCH BYTEL I MUSHELLKA
Streszczenie
Zespół Smart Power od 2012 roku bierze udział w zawodach Shell Eco-marathon.
Od początku zespół pracuje nad zwiększeniem bezpieczeństwa kierowcy na drodze. W
tym artykule skupiono się na porównaniu systemów wykrywających obiekty w martwym
polu zastosowanych w pojeździe Mushellka oraz w projektowanym pojeździe Bytel.
Słowa kluczowe: Martwe pole, bezpieczeństwo kierowcy, Shell Eco-marathon,
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