Adaptive Vehicle Mode Monitoring using Embedded Devices with Accelerometers ∗ Artis Mednis, Georgijs Kanonirs, and Leo Selavo
Abstract Monitoring of specific attributes such as vehicle speed and fuel consumption as well as cargo safety is an important problem for transport domain. This task is performed using specific multiagent monitoring systems. To ensure secure operation of such systems they should have autonomous and adaptive behaviour. The paper is describing an adaptive agent for vehicle mode monitoring using embedded devices with accelerometers. Data processing algorithm and adaptive functionality are discussed and their evaluation is presented with vehicle standing mode detection as high as true positive rate of 97% using real world data. Optimization of parameters for data processing algorithm is performed as well as suggestions for their application described.
1 Introduction Transportation of passengers and cargo is performed using different vehicle types. To ensure economical transportation hardware/software systems are used for monitoring of specific attributes such as selected route, vehicle speed, fuel consumption, drivers’ shift time, cargo safety [3] etc.
∗ This is an author-created version. The final publication is available at www.springerlink.com. 10.1007/978-3-642-28762-6 28
Artis Mednis · Georgijs Kanonirs · Leo Selavo Digital Signal Processing Laboratory, Institute of Electronics and Computer Science, 14 Dzerbenes Str., Riga, LV 1006, Latvia e-mail:
[email protected] Leo Selavo Faculty of Computing, University of Latvia, 19 Raina Blvd., Riga, LV 1586, Latvia
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These systems are designed and developed in manner that minimizes possible manipulations by vehicle driver. One paradigm to achieve this goal is system with maximum autonomy and independence from the monitored vehicle. For example, data about the vehicle speed is acquired not from a vehicle electronic onboard system but from a separate GNSS ([4], p.2) receiver included in monitoring system. Unfortunately there exist possibilities to bypass or sabotage monitoring system, for example, using GNSS jammers. Therefore data acquisition solutions or a ”black box” that are hardened against bypassing or sabotage are necessary. Our developed vehicle mode monitoring solution is meant for use as a single adaptive agent in a multiagent system. It is based on adaptive algorithm and uses as input data only measurements from 3-axis accelerometer. Related work is discussed in Section 2. Technical requirements are listed in Section 3. Main algorithm and adaptive functionality are described and analyzed in Section 4. The final section presents the conclusion that the proposed solution detects vehicle standing mode with 97% reliability and adaptive functionality makes it suitable for different vehicle types.
2 Related Work There are several accelerometer based adaptive multiagent systems for monitoring of different types of objects such as elderly people in their own homes [5, 7, 8] as well as power transformers in transmission substations [1]. All these systems share a common paradigm - a large and complex task is splitted into many small and simple subtasks and distributed between separate agents. Some of these subtasks may require from corresponding agent an adaptive behaviour. There are also several accelerometer based systems which purpose is monitoring of vehicle mode. In the simplest case the main goal of the system is distinguishing between two main vehicle modes - standing and driving [2, 9]. Deciding about actual vehicle mode is performed using predefined signal patterns and thresholds as well as input from additional data sources such as GNSS receivers. More advanced systems not only detect actual vehicle mode but also assist or even temporarily replace common GNSS based vehicle position determination systems [10]. In both cases there is a need for calibration of the system for specific vehicle. Our approach includes not only detecting of actual vehicle mode but also an adaptive functionality what allows the usage of the system in different types of vehicles such as passenger cars and busses.
3 Technical Requirements The research described in this paper was carried out as a feasibility study of a wider in scope industrial research project. This project assumes design of multiagent sys-
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tem with the aim to restrict fuel misappropriation. Therefore, following list of technical requirements was chosen as a basis for vehicle mode monitoring subsystem: 1. The system should be able to detect two vehicle operation modes - driving mode and standing mode, including standing mode with working engine. 2. The system should be able to detect vehicle operation mode while driving on roads covered with asphalt such as city streets as well as intercity highways. 3. The system should be able to detect vehicle operation mode in real time with granularity at least 10 seconds (in optimal case - not exceeding 1 second). 4. The system should be able to run on embedded device with a microcontroller characterized by the following parameters and using not more than 30% of its resources: CPU speed 16 MIPS, program memory 128 KB, RAM 3862 bytes and data EEPROM 1024 bytes. 5. The system should use acceleration measurements as input data from 3-axis accelerometer with range configurable from ±2 g to ±8 g. Connection to vehicle electronic onboard system or use of GNSS receiver are not intended.
4 Our Approach To examine the suitability of 3-axis accelerometer as the only sensor for vehicle mode monitoring a real world experiment was performed. During this experiment passenger car BMW 323 Touring carried out 13.5 km long distance (3 rounds x 4.5 km, Fig. 1 - on the left) in 34 minutes. Accelerometer data acquisition was performed 37x per second using a slightly modified LynxNet collar device developed during our past research activities related to wild animal monitoring using sensor networks [11]. Among other hardware items this embedded device includes Analog Devices 3-axis accelerometer ADXL335. Parallel to accelerometer data acquisition vehicles position and speed data (Fig. 2
Fig. 1 4.5 km long road network fragment used for real world experiment. On the left - marked positions correspond to coordinates where GNSS receiver fixed vehicle speed 0 km/h, on the right - marked positions correspond to coordinates where STDEV algorithm fixed activity level less than 0.04 g.
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- on the left) were collected 1x per second using SBAS capable GNSS receiver Magellan eXplorist XL.
4.1 Main Algorithm Processing of accelerometer data was performed using slightly modified STDEV algorithm from our past research activities related to real time pothole detection using Android smartphones with accelerometers [6]. Original version of the STDEV algorithm includes calculation of accelerometer vertical Z-axis data standard deviation as well as thresholding of calculated values. In this case potholes are detected by acceleration values above specific threshold level. Modified version of the STDEV algorithm includes aggregation of all 3-axis accelerometer data standard deviation values as well as thresholding of acquired aggregate values. In this case vehicle standing mode is detected by acceleration values below specific threshold level. During this proof of the concept experiment sliding window with size 10 samples was used as well as threshold value 0.04 g. Vehicle activity profile created from accelerometer data is shown in Fig. 2 on the right but performance of modified STDEV algorithm in the context of vehicle mode monitoring - in the Table 1. Table 1 Performance of modified STDEV algorithm in the context of vehicle mode monitoring Parameter
Value
All vehicle positions fixed during experiment Standing mode positions fixed using GNSS receiver data Standing mode positions fixed using accelerometer data Standing mode positions fixed using both above mentioned approaches
2043 514 634 500
Vehicle mode monitoring using 3-axis accelerometer data as input and modified STDEV algorithm for data processing allows detecting of 500 from 514 (percent equivalent 97%) standing mode vehicle positions fixed by vehicle speed data from
Fig. 2 Fragments of the speed and activity profiles of the passenger car BMW 323 Touring used for real world experiment. On the left - vehicle standing mode corresponds to places where GNSS receiver fixed vehicle speed 0 km/h, on the right - vehicle standing mode corresponds to places where modified STDEV algorithm fixed activity level less than 0.04 g.
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GNSS receiver. 123 from 134 (percent equivalent 92%) from other vehicle positions detected by this algorithm were characterized by vehicle speed below 25 km/h as well as location in the close proximity to the positions where vehicle speed according GNSS receiver data was 0 km/h (Fig. 1 - on the right). Analysis of the created vehicle activity profile revealed a necessity for a mechanism to prevent vehicle mode detection instability in cases when values of vehicle activity profile are changing in a narrow range near the threshold value. Such mechanism could be established due usage of not only one but already two threshold values - Tlow and Thigh . Other improvements of the algorithm include increasing of the accelerometer data acquisition rate from 37x to 100x per second as well as increasing of sliding window size from 10 samples (0.25 seconds) to 100 samples (1 second). These changes are related to usage of full potential of accelerometer characteristics. The goal of the next real world test was to verify this solution using several vehicle types such as passenger cars and buses, several road surface types such as city streets and intercity highways, several vehicle ”stop” types such as traffic lights and bus stops. These experiments were carried out using following version of the algorithm: 1. Acquisition of 3-axis accelerometer data 100x per second. 2. Storage of all accelerometer data acquired during last second using appropriate data structure: X[] = {Xn−99 , ..., Xn };Y [] = {Yn−99 , ...,Yn }; Z[] = {Zn−99 , ..., Zn }
(1)
3. Calculation of all 3 axis standard deviation values as well as their aggregation: ST DEV (X[]) + ST DEV (Y []) + ST DEV (Z[])
(2)
4. If aggregation of all 3-axis standard deviation values is a. > 0.1g (above Thigh ) then vehicle mode is set to driving; b. < 0.05 g (below Tlow ) then vehicle mode is set to standing; c. ≥ 0.05 g and ≤0.1 g (between Tlow and Thigh ) then previous set vehicle mode is preserved. Analysis of the accelerometer data acquired from bus Setra S415 HDH revealed that activity profile of this vehicle type is characterized by relatively lower g values (Fig. 3). In this case typical value for vehicle standing mode was 0.02 g therefore corresponding values for Tlow and Thigh could be 0.03 g and 0.06 g respectively. Differences in the activity profile could be explained with different vehicle volumes as well as different distances between data acquisition hardware and vehicle engine as most significant source of the vibrations during vehicle standing mode. Following real world experiments were carried out using different hardware platform POGA v.1b consisting of WSN mote Tmote Mini and accelerometer ADXL330 similar to previous used ADXL335. This hardware item includes also
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two LED’s used for visual real time control of currently detected vehicle mode. After analysis of data acquired in three different vehicles (Table 2) the conclusion about suitability of improved algorithm for usage in different vehicle types was taken as well as the necessity for self-calibration functionality to adapt corresponding threshold values for each individual vehicle was approved. Table 2 Performance of vehicle mode detection algorithm in the context of different vehicles using threshold levels Tlow =0.05 g and Thigh =0.1 g.
Standing Driving Stopping Starting
Volvo V70
VW Passat Variant
Setra S415 HDH
OK OK OKa OKb
OK OK OKa OKb
OK NOKc OKd NOKe
a
Switching could be pre-emptive if stopping is performed rolling on flat surface Switching could be delayed if starting is performed smoothly c There is a tendency switch to standing during rolling on flat surface d Switching could be particularly pre-emptive if stopping is performed rolling on flat surface e Switching could be particularly delayed if starting is performed smoothly (typical for busses) b
4.2 Adaptive Functionality Based on the data acquired during previous real world experiments two assumptions were defined as a basis for development of an adaptive functionality for vehicle mode detection algorithm: 1. There exist vehicle activity levels with certain adherence to main vehicle modes. The first approximation of these levels is 0.1 g and more for driving mode as well as 0.03 g and less for standing mode. 2. Certain vehicle standing mode is represented by at least 5 seconds long period characterized by narrow range of vehicle activity level.
Fig. 3 A fragment of the activity profile of the bus Setra S415 HDH used for real world experiment. Vehicle standing mode corresponds to places where modified STDEV algorithm fixed activity level less than 0.03 g.
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Previous version of the algorithm assumes calculation of vehicle activity level 1x per second. This frequency should be preserved in the adaptive version to maintain vehicle mode detection with granularity 1 second. Additional calculations should be performed for detection of certain vehicle standing mode periods and subsequent main algorithm calibration: 1. Storage of all vehicle activity level values acquired during the last 5 seconds using appropriate data structure: ACT IV ITY [] = {ACT IV ITYn−4 , ..., ACT IV ITYn }
(3)
2. Calculation of vehicle activity level standard deviation value after each added value: ST DEV (ACT IV ITY []) (4) 3. If calculated vehicle activity level standard deviation value is under a certain threshold (first approximation of this level is 0.005 g) the counting of consecutive calculations is started or continued. 4. If count of consecutive values under certain threshold reaches 5 and vehicle standing mode period is detected, the arithmetic mean (AM) of corresponding vehicle activity level values is calculated as well as both threshold values Tlow and Thigh in the algorithm changed: Tlow = AM{ACT IV ITY []} × 1.25; Thigh = Tlow × 1.5
(5)
5. To avoid miscalibration of the algorithm due vehicle standing without working engine as well as driving with stable acceleration several additional constraints should be set: Tlow ̸= 0g; Thigh ≤ 0.11g (6) Adaptive version of vehicle mode detection algorithm was tested using passenger car Volvo V70 and bus Setra S415 HDH. Results of these test drives are shown in Fig. 4. Standing and driving modes are properly detected despite different vehicle activity profiles.
Fig. 4 Vehicle activity profiles - on the left Volvo V70, on the right - Setra S415 HDH. Detected vehicle standing mode corresponds to places where adaptive STDEV algorithm fixed activity level under Tlow .
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5 Conclusion We have proposed an adaptive vehicle mode monitoring solution that includes embedded device with a 3-axis accelerometer. The solution was evaluated on a particular application - detection of vehicle standing and driving modes with 1 second granularity. The detection was performed by adaptive thresholding of accelerometer data standard deviation values. We performed several test drives on different road surface types using several vehicle types. The experimental results were evaluated by comparison with the corresponding vehicle speed data acquired from GNSS receiver as well as real time observations using the visual interface of embedded device. The results show, that our solution detects vehicle standing mode with 97% reliability and adaptive functionality allows its usage in different vehicle types characterized by different vehicle activity profiles. The future work includes evaluating of the developed solution using broader vehicle type set and improvement of vehicle mode switching detection using characteristic activity patterns of particular vehicle and driver. Acknowledgements This work was supported by European Social Fund grant Nr. 2009/0219/1DP/ 1.1.1.2.0/APIA/VIAA/020 ”R&D Center for Smart Sensors and Networked Embedded Systems”.
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