Volume 17 Number 6
December 2020
Article Contents
K.A. Venkatesh, N. Mathivanan. Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles. International Journal of Automation and Computing, 2020, 17(6): 883-890. doi: 10.1007/s11633-016-0968-4
Cite as: K.A. Venkatesh, N. Mathivanan. Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles. International Journal of Automation and Computing, 2020, 17(6): 883-890. doi: 10.1007/s11633-016-0968-4

Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles

Author Biography:
  • N.Mathivanan  received his B.Sc.degree in physics from Madurai University, India in 1975, M.Sc.degree in applied physics from the University of Madras, India, in 1977, M. Tech.degree in Instrumentation Technology from Indian Institute of Science, India in 1995 and the Ph.D.degree from Madurai Kamaraj University, India, in 1998.In 1982, he was a lecturer at University Science Instrumentation Centre, Madurai Kamaraj University, India. From 1998, he is the director of University Science Instrumentation Centre, Madurai Kamaraj University, India.Currently, he is the professor & director in the University Science Instrumentation Centre.He has published about 30 refereed journal and conference papers.He has authored two textbooks.Prof.N.Mathivanan received Life-Time Achievement Award for the year 2012 from Nehru group of institutions, Coimbatore, India.
    His research interests include PC based instrumentation and wireless sensor networks.
    E-mail:nmvanan@yahoo.com

  • Corresponding author: K.Arun Venkatesh   received the B.Sc.degree in electronics and M.Sc. degree in instrumentation from the Madurai Kamaraj University, India in 2003 and 2005, respectively.Currently, he is a researcher in University Science Instrumentation Centre, Madurai Kamaraj University, India.He has published about 10 refereed journal and conference papers.
    His research interests include in-vehicle networks, MEMS, advanced embedded systems and virtual instrumentation.
    E-mail:arunvenkateshk@gmail.com (Corresponding author)
  • Received: 2013-06-14
  • Accepted: 2015-05-14
  • Published Online: 2020-06-20
  • Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system (MEMS) accelerometer, an MEMS gyroscope, an advanced RISC machines (ARM) CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteristics of the network and the measurement system are studied and reported.
  • Recommended by Associate Editor Kai Cheng
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  • [1] N. Navet, Y. Q. Song, F. Simonot-Lion, C. Wilwert. Trends in automotive communication systems. Proceedings of the IEEE, vol. 93, no. 6, pp. 1204-1223, 2005.  doi: 10.1109/JPROC.2005.849725
    [2] H. Kopetz, W. Elmenreich, C. Mack. A comparison of LIN and TTP/A. In Proceedings of IEEE International Workshop on Factory Communication Systems, IEEE, Porto, USA, pp. 99-107, 2000.
    [3] F. Baronti, E. Petri, S. Saponara, L. Fanucci, R. Roncella, R. Saletti, P. D. Abramo, R. Serventi. Design and verification of hardware building blocks for high-speed and fault-tolerant in-vehicle networks. IEEE Transactions on Industrial Electronics, vol. 58, no. 3, pp. 792-801, 2011.  doi: 10.1109/TIE.2009.2029583
    [4] TTTech Computertechnik AG. Time-triggered Protocol TTP/C High Level Specification Document, Protocol Version 1.1, Technical Report, TTAgroup, 2003.
    [5] W. Elmenreich, R. Ipp. Introduction to TTP/C and TTP/A. In Proceedings of the Workshop on Time-triggered and Real-time Communication Systems, pp. 1-9, 2003.
    [6] H. Kopetz, M. Holzmann, W. Elmenreich. A universal smart transducer interface: TTP/A. In Proceedings of Third IEEE International Symposium on Object-oriented Real-time Distributed Computing, IEEE, Newport, CA, USA, pp. 16-23, 2000.
    [7] H. Kopetz. A Comparison of TTP/C and FlexRay. Research Report 10/2001, Institute of Computer Engineering, Vienna University of Technology, Wein, Austria, 2001.
    [8] A. Albert. Comparison of event-triggered and time-triggered concepts with regard to distributed control systems. In Proceedings of Embedded World, Nuremberger, Germany, pp. 235-252, 2004.
    [9] A. Ademaj, H. Kopetz, P. Grillinger, K. Steinhammer. Integration of Predictable and Flexible In-Vehicle Communication Using Time-triggered Ethernet, [Online], Available: http://www.vmars.tuwien.ac.at, June 11, 2013.
    [10] L. L. Bello. The case for ethernet in automotive communications. In Proceedings of SIGBED Review, Special Issue on the 10th International Workshop on Real-time Networks (RTN 2011), ACM, New York, USA, vol. 8, no. 4, pp. 7-15, 2011.
    [11] R. O. Ocaya, J. Minny. A TCP/IP framework for ethernet-based measurement, control and experiment data distribution. Journal of Instrumentation, vol. 5, 2010.
    [12] R. O. Ocaya. A framework for collaborative remote experimentation for a physical laboratory using a low cost embedded web server. Journal of Network and Computer Applications, vol. 34, no. 4, pp. 1408-1415, 2011.
    [13] P. Doležel, V. Vašek, D. Janáčová, K. Kolomazník, M. Zálešák. Modeling and microcontroller control of raw hide soaking in tannery industry. International Journal of Mathematical Models and Methods in Applied Sciences, vol. 5, no. 7, pp. 1225-1232, 2011.
    [14] I. Ahmed, H. Wong, V. Kapila. Internet-based remote control using a microcontroller and an embedded Ethernet. In Proceedings of the American Control Conference, IEEE, Boston, MA, USA, vol. 2, pp. 1329-1334, 2004.
    [15] K. Muller, T. Steinbach, F. Korf, T. C. Schmidt. A real-time Ethernet prototype platform for automotive applications. In Proceedings of IEEE International Conference on Consumer Electronics, IEEE, Berlin, Germany, pp. 221-225, 2011.
    [16] LPY530AL MEMS motion sensor: dual axis pitch and yaw ±300°/s analog gyroscope. STMicroelectronics, Doc ID 15807 Rev 2, July 2009.
    [17] ADXL335: Small, Low Power, 3-Axis±3 g Accelerometer. Analog Devices, Rev. A, 2009.
    [18] B.V. NXP (founded by Philips). UM10360 –LPC17xx User Manual, Rev. 2C, 19 August 2010.
    [19] DP83848C PHYTER-Commercial Temperature Single Port 10/100 Mb/s Ethernet Physical Layer Transceiver, National Semiconductor, May 2008.
    [20] Compex Systems. PS2208-Product Datasheet, 2006.
    [21] G. Welch, G. Bishop. An Introduction to the Kalman Filter, TR-95-041, Department of Computer Science, University of North Carolina at Chapell Hill, USA 2002.
    [22] M. Haid, J. Breitenbach. Low cost inertial orientation tracking with Kalman filter. Applied Mathematics and Computation, vol. 153, no. 2, pp. 567-575, 2004.  doi: 10.1016/S0096-3003(03)00656-8
    [23] K. H. Yang, W. S. Yu, X. Q. Ji. Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera. International Journal of Automation and Computing, vol. 9, no. 3, pp. 292-298, 2012.  doi: 10.1007/s11633-012-0647-z
    [24] K. A. Venkatesh, N. Mathivanan. Design of MEMS accelerometer based acceleration measurement system for automobiles. Measurement Science Review, vol. 12, no. 5, pp. 189-194, 2012.
    [25] K. A. Venkatesh, N. Mathivanan. CAN network based longitudinal velocity measurement using accelerometer and GPS receiver for automobiles. Measurement Science Review, vol. 13, no. 3, pp. 115-121, 2013.  doi: 10.2478/msr-2013-0020
    [26] W. T. Higgins. A comparison of complementary and Kalman filtering. IEEE Transactions on Aerospace and Electronic Systems, vol. AES-11, no. 3, pp. 321-325, 1975.  doi: 10.1109/TAES.1975.308081
    [27] S. Colton, F. R. C. Mentor. The balance filter. Presentation, Massachusetts Institute of Technology, 2007.
    [28] SiRF Technology Inc. NMEA Reference Manual, Rev. 1.3 January 2005.
    [29] Cyber i-Technologies Co., Ltd. GPS-634R Technical Data Sheet, Ver. 1.4, 2010.
    [30] S. Biaz, N. H. Vaidya. Is the round-trip time correlated with the number of packets in flight? In Proceedings of IMC'03, Miami Beach, Florida, USA, 2003.
    [31] A. Dannenberg. SLAA137A –MSP430 Internet Connectivity. Texas Instrument Application Report. Rev-A. February 2004.
    [32] S. Shon. Protocol implementations for web based control systems. International Journal of Control, Automation, and Systems, vol. 3, no.1, pp. 122-129, 2005.
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Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles

  • Corresponding author: K.Arun Venkatesh   received the B.Sc.degree in electronics and M.Sc. degree in instrumentation from the Madurai Kamaraj University, India in 2003 and 2005, respectively.Currently, he is a researcher in University Science Instrumentation Centre, Madurai Kamaraj University, India.He has published about 10 refereed journal and conference papers.
    His research interests include in-vehicle networks, MEMS, advanced embedded systems and virtual instrumentation.
    E-mail:arunvenkateshk@gmail.com (Corresponding author)

Abstract: Design of an Ethernet network compatible data acquisition system for the measurement of yaw rate and longitudinal velocity in automobiles is presented.The data acquisition system includes a base node and a remote node.The remote node consists of a micro electro mechanical system (MEMS) accelerometer, an MEMS gyroscope, an advanced RISC machines (ARM) CORTEX M3 microcontroller and an Ethernet PHY device.The remote node measures the yaw rate and the longitudinal velocity of an automobile and sends the measured values to the base node using Ethernet communication.The base node consists of an ARM CORTEX M3 microcontroller and an Ethernet PHY device.The base node receives the measured values and saves in a microSD card for further analysis.The characteristics of the network and the measurement system are studied and reported.

Recommended by Associate Editor Kai Cheng
K.A. Venkatesh, N. Mathivanan. Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles. International Journal of Automation and Computing, 2020, 17(6): 883-890. doi: 10.1007/s11633-016-0968-4
Citation: K.A. Venkatesh, N. Mathivanan. Design of Ethernet Based Data Acquisition System for Yaw Rate and Longitudinal Velocity Measurement in Automobiles. International Journal of Automation and Computing, 2020, 17(6): 883-890. doi: 10.1007/s11633-016-0968-4
  • Electronic control units (ECU) are being employed in automobiles to facilitate steering control, traction (i.e., control of the driving torque), antilock braking system (ABS), electronic stability control (ESC), electric power steering (EPS), active suspensions, or engine control. ECUs are used to control lights, wipers, doors, windows, entertainment and communication equipment (e.g., radio, DVD, hands-free phones, navigation systems) also. An ECU is a subsystem composed of a microcontroller and a set of sensors and actuators designed for a particular task such as measurement of a parameter, control of an actuator, etc. The ECUs are located at different parts of the automobile. To support exchange of information between ECUs and to improve the overall performance of the systems, the ECUs may be networked[1].

    A design of ESC system that prevents possible oversteering or understeering in automobiles, uses multiple ECUs to monitor parameters such as longitudinal velocity, lateral velocity, yaw rate, wheel speed, steering wheel angle, etc., and to control the brakes of each wheel. Hence an ESC system should use a high speed network for communication between ECUs.

  • Network protocols such as controller area network (CAN), local interconnect network (LIN)[2], FlexRay[3], media oriented systems transport (MOST), time triggered protocol-A (TTP/A)[4, 5], time triggered protocol-C (TTP/C)[6, 7], etc. are generally used for in-vehicle communication. Table 1 gives the characteristics of the above protocols and Ethernet protocol. The networks are either event-triggered or time-triggered. Event-triggered networks operate on "first-come-first-serve" basis and higher priorities are assigned to critical messages. Event-triggered networks are simple and are easy to implement. The main advantage of event-triggered systems is their ability to react to asynchronous external events very quickly. Thus, they have a better real-time performance compared with time-triggered systems[8]. CAN, LIN, and MOST are event-triggered networks. Time-triggered networks use time division multiple access (TDMA) technique for bus arbitration. Time-triggered networks are used in systems that need predictability and fault tolerance. Critical and safety systems require time-triggered networks. But they lack flexibility and scalability. Small changes in one subsystem may lead to design of a complete new system. TTP/A and TTP/C are time-triggered networks. FlexRay use time-triggered data transfer for time critical messages and event-triggered data transfer for other messages.

    Table 1.  Characteristics of few in-vehicle network protocols and Ethernet protocol

  • Ethernet is a network protocol used in local area networks. Ethernet was introduced in 1980 and now it has the benefits of established infrastructure, high performance, high bandwidth, cross-platform compatibility and inter-operability. Since the Ethernet protocol is inherently non-deterministic, it is not generally used in real-time and safety critical systems. It prevents Ethernet from replacing the field buses used in control applications including vehicle automation. However, large numbers of real-time Ethernet (RTE) solutions have now been developed. These solutions try to guarantee deterministic and/or timely delivery of messages while keeping the benefits of Ethernet. POWERLINK, PROFINET, EtherCAT, EtheReal, and TT Ethernet[9] are some examples of RTE. Many new vehicle automation applications such as advanced driver assistance systems require high bandwidth to support high speed data communication. Ethernet is more suitable than the existing in-vehicle networks for high speed and high bandwidth applications[10]. Hence, Ethernet usage in vehicle automation is expected to grow in future. Its relative cost over other interfaces has made it an attractive solution for many measurement and control applications.

    Internet based data acquisition systems using microcontrollers have already been developed for remote data acquisition[11-14]. These systems employ the transmission control protocol/internet protocol (TCP/IP) over Ethernet. In case of in-vehicle communication, the data transfer will be between very few known nodes and the use of TCP/IP protocol is limited. Hence, a raw Ethernet network using microcontrollers is developed. A raw Ethernet packet consists of a 7 byte preamble, 1 byte start of frame delimiter, a 6 byte media access controller (MAC) source address, 6 byte MAC destination address, 2 byte type/length packet, 46–1500 data bytes and a 4 byte checksum. MAC addresses instead of internet protocol (IP) addresses can be used to identify each node. Similarly, microcontroller based systems can fulfill the requirements of real-time applications and are suitable for safety critical applications[15].

    In the present study, an Ethernet compatible high speed microcontroller based data acquisition system has been designed and constructed for the measurement of yaw rate and longitudinal velocity of an automobile using micro electro mechanical system (MEMS) gyroscope and MEMS accelerometer.

  • The data acquisition system consists of a remote node and a base node attached to an Ethernet compatible network. The functional block diagram is shown in Fig. 1.

    Figure 1.  Functional block diagram of the data acquisition system

    The remote node includes an MEMS gyroscope (LPY530AL), a three axis MEMS accelerometer (ADXL335), associated signal conditioning circuits, an ARM CORTEX M3 microcontroller (LPC1768), an ethernet physical layer transceiver (DP83848C) and a RJ45 connector. The circuit diagram of the data acquisition system is shown in Fig. 2. The MEMS gyroscope is a dual axis gyroscope LPY530AL, which measures the angular velocity over $ Y $ and $ Z $ axes. The gyroscope provides two output lines (OUT and 4xOUT) with sensitivities of 0.83 mV/°/s and 3.33 mV/°/s respectively[16] for each axis. The output line 4xOUTZ line is used in the present system to measure the yaw rate. The MEMS accelerometer is used to measure the acceleration of the automobile in longitudinal and lateral axes. The accelerometer has a sensitivity of 30.61 mV/m/s2[17]. The accelerometer and gyroscope outputs are buffered and filtered using anti-aliasing filters with a cut-off frequency of 100 Hz. The output of the anti-aliasing filters are applied at the analog input terminals of the LPC1768 microcontroller. The LPC1768 microcontroller has eight analog input channels and a 12-bit ADC operating with 3.3 V reference voltage. It also has an Ethernet MAC controller with DMA support for high speed data transfer[18]. Since the ADC operates with +3.3 V reference, the resolution of the 12-bit ADC is 0.805 9 mV. Hence, the measurement system has the resolution of 0.0239 m/s2 in linear and longitudinal acceleration measurements and 0.244°/s in angular velocity measurements. The Ethernet transceiver DP83848C is connected to the Ethernet I/O lines of the microcontroller. It is a 10/100 single port physical layer device[19].

    Figure 2.  Circuit diagram of the data acquisition system

    The base node consists of a LPC1768 microcontroller, an Ethernet transceiver DP83848C, a RJ45 connector and a microSD card. The remote node and the base node are connected to an 8-port Ethernet L2 switch, Compex PS2208B using CAT5 UTP cables. The L2 switch has a backplane capacity of 1.6 Gbps with an average allocation of 200 Mbps per port, allowing every node to perform at their maximum performance[20].

    The remote node measures the longitudinal velocity and yaw rate of the automobile and transfers the data to the base node on the network. The procedure for estimating the longitudinal velocity and the yaw rate is given in Section 4. The received data are saved in the microSD card by the base node for further analysis.

  • The longitudinal acceleration, $ a_X (t) $ and the lateral acceleration, $ a_Y (t) $ of the vehicle are computed as

    $ \begin{align} a_X \left( t \right) = \left( {\left( {R\times Q_X\left(t \right)} \right)-offset_X } \right)\times \frac{1}{S_A } \end{align} $

    (1)

    $ \begin{align} a_Y \left( t \right) = \left( {\left( {R\times Q_Y\left(t \right)} \right)-offset_Y } \right)\times \frac{1}{S_A }\; \end{align} $

    (2)

    where $ R $ is the resolution of ADC which is 0.805 9 mV, $ Q_X (t) $ and $ Q_Y (t) $ are quantized accelerometer $ X $-output and $ Y $-output respectively, $ offset_X $ and $ offset_Y $ are zero-g offset voltage for accelerometer $ X $-output and $ Y $-output in volts and $ S_A $ is the sensitivity of the accelerometer in V/ms$ ^{-2} $.

    The longitudinal velocity, $ v_X (t) $ of the vehicle is computed by integrating $ a_X (t) $ as

    $ \begin{align} v_X (t) = \int_0^t {a_X (t)\;{\rm d}t}. \end{align} $

    (3)

    The yaw rate of the vehicle, $ \varphi _A (t) $ is computed using the accelerometer measurements as given below:

    $ \begin{align} \varphi _A \left( t \right) = \left[ {\arctan \left( {\frac{a_Y (t)}{v_X (t)}} \right)} \right]\times \frac{180}{\pi }. \end{align} $

    (4)

    The yaw rate of the vehicle $ \varphi _G (t) $ is computed using the gyroscope measurements as given below:

    $ \begin{equation} \varphi _G \left( t \right) = \left( {\left( {R\times Q_\varphi \left( t \right)} \right)-offset_\varphi } \right)\times \frac{1}{S_G }\; \end{equation} $

    (5)

    where $ Q_\phi (t) $ is the quantized gyroscope $ Z $-output, $ offset_\phi $ is the offset voltage for gyroscope $ Z $-output in V and $ S_G $ is the sensitivity of the gyroscope in V/°/s.

    The angular displacement, $ \psi _G (t) $ is obtained by integrating $ \varphi _G (t) $ assuming initial velocity, $ v_0 $ as zero, and it is given as

    $ \begin{align} \psi _G (t) = \int_0^t {\varphi _G (t)\;{\rm d}t} +v_0. \end{align} $

    (6)

    In discrete time sampled data system, the measurements include additive random noises. A Kalman filter is generally used to reduce random noises and it is extensively used in the area of autonomous or assisted navigation[21-23]. The Kalman filter and its use in the longitudinal velocity estimation have already been discussed in [24, 25]. Though the Kalman filter can be used to estimate angular displacement, a complementary filter is used in the present study to estimate the angular displacement, $ \psi _E (t) $ of the vehicle using $ \phi _A (t) $ and $ \phi _G (t) $. A brief description of complementary filter is given below.

    Complementary filter. A complementary filter is actually a steady-state Kalman filter often used to combine measurements from multiple sensors. While the Kalman filter works in the time domain, the complementary filter works in the frequency domain. Implementation of complementary filter does not require statistical description of the noise corrupting the signals and it involves less computation[26, 27]. The filter function is obtained by a simple analysis in the frequency domain. The technique of the realization of complementary filter and estimation of angular displacement is illustrated in Fig. 3.

    Figure 3.  Complementary filter

    The angular displacement, $ \psi _E (t) $ is estimated with the complementary filter equation as given below

    $ \begin{align} \psi _E (t) = \left[ {G\times \left( {\psi _E (t)+\left( {T_s \times \phi _G (t)} \right)} \right)} \right]+\left[ {\left( {1-G} \right)\times \phi _A (t)} \right] \end{align} $

    (7)

    where $ G = \frac{1}{0.062s+1} $ is the transfer function of first order high pass filter and $ T_s $ is the sampling time.

  • The software for the measurement system is developed on Embedded Workbench Integrated Development Environment developed by IAR systems. Two separate programs are developed and are downloaded into the remote node and the base node.

  • The program in the remote node performs the following tasks in sequence.

    1) Initialize the Ethernet transceiver device and wait till the Ethernet link is "ON".

    2) Configure the Ethernet interface to 10 Mbps data rate, full duplex, 1536 maximum length of bytes per transfer and 15 times maximum retransmission attempts.

    3) Initialize the MAC address as 10:00:00:00:00:02.

    4) Sample the analog input channels at the rate of 250 samples per second. The on-chip free running timer is configured to sample the signals at the required sampling rate.

    5) Calculate the longitudinal acceleration, $ a_X (t) $ and lateral acceleration, $ a_Y (t) $ using (1) and (2) respectively.

    6) Filter the longitudinal and lateral acceleration values using Kalman filter algorithm and estimate the longitudinal velocity, $ v_X (t) $in ms$ ^{-1} $ using (3).

    7) Calculate the yaw rates $ \phi _A (t) $ and $ \phi _G (t) $ using (4) and (5) respectively.

    8) Combine the yaw rates $ \phi _A (t) $ and $ \phi _G (t) $ using complementary filter (7) to estimate angular displacement, $ \psi _E (t) $.

    9) Transfer the measurement data to the base node on Ethernet port.

    10) Repeat Steps 4) to 9) for transfer of next measurement data until "STOP" command is received from base node.

    11) Terminate sampling of signals and transfer of data.

  • The program in the base node executes the following tasks in sequence.

    1) Initialize the ethernet interface with the same settings as done in the remote node. Initialize the MAC address as 10:00:00:00:00:01.

    2) Receive the measurement data sent by the remote node.

    3) Save the data in the microSD card.

    4) Repeat Steps 2) and 3) till a test cycle completes.

    5) Send "STOP" command to remote node.

  • The longitudinal velocity and angular displacement measurements using the presented measurement system and the measurements using a position recording system are compared. A position recording system using a GPS634R GPS receiver, a LPC1768 microcontroller and a 1 GB microSD card has been designed and constructed. The design of position recording system using GPS receiver will be discussed in a future paper. The GPS receiver transmits position (latitude & longitude), velocity $ v_P (t) $, course angle $ \psi _P (t) $, coordinated universal time (UTC) data in NMEA 0183 version 3.01 standard[28] with one second interval through serial port at 9600 baud rate[29]. The GPS receiver is connected to the serial port of the LPC1768 microcontroller through a RS232 driver/receiver (MAX232). The LPC1768 microcontroller receives the GPS receiver output data and saves in the microSD card.

    The measurement system and the position recording system are mounted on a vehicle and the vehicle is driven on a test track shown in Fig. 4(a). The test drive consists of a forward and a return journey. The yaw rates measured using the gyroscope $ \phi _G (t) $ and the accelerometer $ \phi _A (t) $ are shown in Fig. 4(b). The angular displacement measurements $ \psi _P (t) $ (measured using position recording system), $ \psi _G (t) $ (measured using gyroscope) and $ \psi _E (t) $ (computed using complementary filter) are shown in Fig. 4(c). It is observed that $ \psi _E (t) $ is stable and $ \psi _G (t) $ drifts. The drift in $ \psi _G (t) $ is due to integration of noise present in $ \phi _G (t) $ measurement. However, in slalom maneuver where the angular displacement values change very rapidly, the measurements $ \psi _G (t) $ and $ \psi _E (t) $ are better to be compared with $ \psi _P (t) $ since the GPS data are updated at a low frequency of 1\, Hz. Fig. 4(d) shows the longitudinal velocities, i.e., $ v_X (t) $ measured using accelerometer, and $ v_P (t) $ using GPS receiver, during the test drive.

    Figure 4.  Experimental Results. (a) Path of the automobile in the test track; (b) Yaw rate measured using gyroscope $ \phi _G (t) $ and accelerometer $ \phi _A (t) $; (c) Angular displacement estimated using complementary filter $ \psi _E (t) $, measured using position recording system $ \psi _P (t) $ and gyroscope $ \psi _G (t) $; (d) Longitudinal velocity measured using accelerometer $ v_X (t) $ and position recording system $ v_P (t) $.

  • Round trip time (RTT) is a key parameter that indicates the speed of data communication in a network[30]. RTT is the time required for a packet to travel from a specific source node to a destination node and back again in a network. Round trip time has been computed for three different scenarios.

    Scenario 1. A simple program to find RTT has been developed and downloaded into the base node. The program transmits a 64-byte string to remote node and waits till the same string is received. The on-chip timer in LPC1768 microcontroller is used to find the RTT. Another program has been developed and downloaded into the remote node to receive and echo the string. The network uses the raw Ethernet protocol for communication.

    Scenario 2. A program to request measurement data from remote node is developed and executed in the base node. Another program to sample AD0, AD1 and AD2 analog channels and transmit the measurement data to base node on receipt of request from base node is developed and executed in the remote node. The time taken (RTT) for the base node to send a request and receive the response data is found using the on-chip timer.

    Scenario 3. A TCP/IP based internet control messages protocol (ICMP) request service program is developed[31, 32] and downloaded into the remote node. A desktop computer is used to send ICMP request to the remote node using the "ping" command. A data payload of 64 bytes is sent with the request. The "ping" command finds and displays the RTT using the system timer.

    The statistical study on the RTT measurements in the three different scenarios has been carried out by taking 1000 measurements in each scenario and is summarized in Table 2.

    Table 2.  Statistical summary of 1000 RTT measurements in three different scenarios

    It is observed that the average and the variance of RTT in Scenario 3 are higher than RTT in Scenarios 1 and 2. The average RTT in Scenario 3 is large, because the ICMP uses TCP/IP protocol which requires additional processor execution time. The high variance in Scenario 3 shows the unpredictable nature of the TCP/IP.

  • An Ethernet based data acquisition system has been designed to measure the yaw rate and the longitudinal velocity of a vehicle using MEMS gyroscope and MEMS accelerometer. It supports raw Ethernet packets. The angular displacement and the longitudinal velocity has been estimated using complementary filter and Kalman filter respectively. The measurements from the presented system are compared with measurements from a GPS receiver. The complementary filter used in the measurement system to estimate angular displacement removes the drift in measurement using gyroscope. The characteristics of the network are studied by measuring RTT. It shows that raw Ethernet network has less RTT compared with TCP/IP based networks.

    Though CAN, LIN, FlexRay, MOST, TTP/C and TTP/A are generally used for in-vehicle communication, it is shown that the Ethernet network which has comparable features can also be used for in-vehicle communication.

Reference (32)

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