We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.
Automobiles are by now indispensable to our personal lives, as well as to the activities of business, public services, and even national security, but the problem of car thefts is a reality and it threatens the automobile security seriously. According to the National Insurance Crime Bureau, a vehicle is stolen every 25 seconds in the U.S.A. Each year, over 1.2 million vehicles are stolen across the country, causing the loss of 8 billion US dollars. Therefore, the work on vehicle security is significant.
Not surprisingly, many classical intelligent technologies are already well established within the automotive industry for vehicle security. GM has developed the
In the past decade, a number of groups have participated in the research of intelligent vehicles, which have led to projects including ARGO [
In recent years, the paradigm of learning human behaviors has attracted considerable amount of attention. It is difficult to describe the desired instructions into specific and proper code statements. In the past decade, several researchers have proposed various experimental designs and applications [
Support vector machine (SVM) has recently become popular in the machine learning. SVM is a new learning-by-example paradigm spanning a broad range of classification, regression, and density estimation problems. This systematic approach motivated by statistical learning theory combines ideas from various scientific branches such as mathematical programming, exploiting the quadratic programming for convex optimization, functional analysis, indicating adequate methods for kernel representations, and machine learning theory, exploring the large maximum classifiers concept [
SVM has been applied to many areas, such as pattern recognition, regression, equalization [
In this paper, we focus on the research of utilizing dynamic human behavior models for vehicle security (preventing vehicles from being stolen) application. A methodology of modeling dynamic human behaviors is proposed. By learning from driving performances, the intelligent classifier can be embedded into an IC-based car key, through which the vehicle security system can identify valid drivers based on the ways the vehicle are driven and the drivers behave. When an illegitimate driver come to use the car and the demonstrated driving behaviors do not match the specified model, the car will be enabled to automatically stop running and deliver alarm signals accordingly. In [
We highlight the following aspects of our system in this paper: First, live biometrical features in dynamic human behaviors are adopted in the system, which brings the enhanced security to the proposed security system. Second, since we collect the signals directly from human driving controls, which include steering, acceleration and braking, we do not utilize other car dynamics and environmental variables such as the car's yaw angle with respect to the road, lateral offset to the road's center, and the road curvature. Therefore, no complicated sensor is required, which brings to the system robust and efficient performance in realtime. Third, the intelligent security system is easy to install on a normal vehicle by adding on functional modules. No complicated requirements means little space and time needed for system installation and drivers are likewise not distracted by the addition of the in-car system. Finally, we develop a methodology to capture and analyze the characteristics of human behaviors into computational representations. It is easily scalable for other applications.
The paper is organized as follows. Firstly, technical descriptions of the experimental hardware and software platforms are presented. In Section
In this section, the technical descriptions of the implemented hardware and software platforms are presented. Figure
Architecture of the experimental system.
Figure
Architecture of the system hardware implementation.
Figure
Diagram of the simulated driving system.
In the simulation subsystem, we apply one PC to offer the simulated driving environment for the driver, including rendered 3-D graphics display as well as realistic controls, which are the steering wheel and pedals for acceleration and brake. We adopt a set of commercial racing game controllers from Logitech (in Figures
(a) Steering wheel; (b) Acceleration and brake pedals; (c) Simulated driving scene; (d) Processor circuit board.
In the data capturing subsystem, a processor circuit board (in Figure
The
Human driving behavior data.
In the human behavior analysis subsystem, the methods introduced in the following sections are applied to the retrieved human behavior data. For our goal in identifying the drivers from their driving skills, the human behavior model library of each driver is generated from the corresponding behavior data input. Once the models are ready, we implement them as the classifier in the system in response to the real-time individual driving performance.
Before modeling human driving behaviors, we apply data preprocessing methods towards data collected from the previous subsystem. Fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) are investigated in this paper. The output of this data preprocessing module is for the support vector machine (SVM) modeling and evaluation. These methodologies are presented as follows.
In this section, we apply data preprocessing methods towards data collected from the aforementioned experimental platform. It is necessary and important to apply data reduction and feature selection in data preprocessing for human behaviors modeling because failures in feature selection reduces the efficiency of the system performance significantly, even bad feature selection causes the failure of whole recognition procedures. Among several feature extraction methods, fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) are investigated in this paper.
To determine the extent of preprocessing human behaviors, we consider factors such as the existence of a preprocessing algorithm, its necessity, its complexity, and its generality. We select fast Fourier transform. In fact, if we have a function given by
Although the Fourier transform does not explicitly show the time localization of frequency components, the time localization can be presented by suitably prewindowing the signal in time domain [
STFT at time
The method can be described in brief as follows suppose that we have two sets of training samples: For a certain eigenvector Train a classifier If
Apart from PCA, we also propose using independent component analysis (ICA) to reduce the dimensions of the data inputs for human behavior modeling. Independent component analysis is a statistical method which transforms an observed multidimensional vector into components that are statistically as independent as possible.
A fixed-point algorithm is employed for independent component analysis [ Generate an initial random vector Stop if converged (
If the process converges successfully, the vector
In this paper, SVM is applied within the framework of modeling human behaviors for intelligent vehicle security application. Inherent complexities and the nonlinearity of human dynamic behavior make mathematical modeling difficult, hindering the use of conventional methods for process modeling and condition monitoring.
In SVM, the basic idea is to map the data
In (
We propose to use SVM to model human driving behaviors. We consider human driving data
Block diagram of SVMs network classifier.
In any predictive learning task, such as classification, an appropriate representation of examples as well as the model and parameter estimation method should be selected to obtain a high level of performance of the learning machine. Traditional statistical approach to estimating models from data is based on parametric estimation. The basic fact that an assumption of an underlying dependency with a simple known parametric form is an ensuing need, limits its applicability in practice. Recent approaches allow a wide class of models of varying complexity to be chosen. The task of learning then amounts to selecting the model of optimal complexity and estimating parameters from training data. Under the SVM approach, the parameters to be usually chosen are the following. The penalty term The mapping function The kernel function such that
In this section, we conduct experiments based on the proposed methodology for recognition of driver identities by analyzing the driving performances. In order to estimate the performance of the proposed system, we invite 7 human subjects to attend the experiment, who are Meng, Ou, Ye, Huang, Wang, Wu, and Shen. They are asked to “drive" on the designed experimental platform individually. The raw data of their driving behaviors is collected by the Data Sensing and Capturing Subsystem. The data recorded is to be analyzed by the Data Analysis Subsystem aforementioned. Our objective is to identify the driving data by trained SVM models. We use the accuracy rate of the SVM classifications to evaluate the performance of the proposed system. The experimental results of applying different data preprocessing methods and choosing different parameters of SVM when modeling human driving behaviors are shown in what follows.
In the first series of experiments, we run different data preprocessing methods for the optimization. The raw data is captured at a rate of 10 Hz and overlapping windows are applied on the data to cut the data into segments. Each segment is 40 seconds long and can be considered as a matrix of size
We then apply FFT, PCA, and ICA to reduce the input size to the SVM for classification. The following steps are performed on each data segment. Apply FFT of order 20 to transform each column of data of size 400 into 20, so the result retrieved is a matrix of size Divide the raw data matrix into 10 parts by time sequence and align these Divide the raw data matrix into 10 parts by time sequence and align these
We compare the data preprocessing using PCA and ICA with FFT. We simply train an SVM to distinguish one tester from all testing data, which is Meng's, to evaluate the performances of three methods of feature selection and data reduction. We have 2 groups of data containing 348 raw data segments totally, 104 segments representing the behaviors of driver Meng (the authorized driver) and 244 segments representing non Meng (the unauthorized drivers). These segments are sent to SVM for training and testing. Due to the aforementioned rules, each segment is processed to a
Three data preprocessing methods are tested independently and the SVM testing results are shown in Table
Test results using different preprocessing methods.
Number of errors | Number of errors | Average success rate | |
(authorized driver) | (unauthorized drivers) | ||
FFT | 22 | 46 | 80.46% |
PCA | 39 | 82 | 65.23% |
ICA | 47 | 79 | 63.79% |
Next we examine the different parameters of FFT to the classification results. Table
Test results using different sampling time and FFT orders.
Sampling time | FFT (order 5) | FFT (order 10) | FFT (order 20) |
---|---|---|---|
10 sec. | 64.92% | 66.83% | 64.68% |
20 sec. | 66.51% | 66.51% | 60.29% |
40 sec. | 69.23% | 69.23% | 78.85% |
80 sec. | 63.46% | 55.77% | 51.92% |
160 sec. | 65.38% | 53.85% | 65.38% |
Table
Training SVM requires the selection of parameters which influence the ensuing model performance. Therefore, to achieve a good model those parameters have to be chosen correctly. Examples, as stated earlier, are
In this series of experiments, we run the SVM classifier with several values of
Success rate of model Meng versus Others parameterized with
(a) Number of SVs versus
In the second part of our experiments, we consider a polynomial kernel as the kernel function. It has the property that
Figure
Success rate of model Meng versus Others parameterized with
(a) Number of SVs versus
From the results shown above, larger
Due to the requirements of the proposed system, we aim to achieve a high classification accuracy as well as low computational consumption. The aim of the identification system is for the vehicle to judge if it is his own driver, so we set the Meng's success rate with higher priority. It is found experimentally that
Test results on driver identifications via SVM.
Meng | Ou | Ye | Huang | Wang | Wu | Shen |
---|---|---|---|---|---|---|
81.45% | 93.62% | 77.08% | 84.62% | 79.31% | 90.00% | 89.47% |
In this section, we demonstrate that SVM is a feasible parametric model for our proposed application. The first aspect investigated is to use preprocessing methods for feature extraction from large human dynamic behavior data for modeling purposes. The extension of the implementation is to the data sets in a larger scale and different methods of problem multiclass formulation. The feature extraction method based on FFT is found to be able to give the best data reduction results compared to PCA and ICA in the presented experiments of modeling human driving behaviors through SVM. FFT establishes a one-to-one mapping between the time domain and the frequency domain and preserves information from the original signal, ensuring that important features are not lost as a result of the transformation. Under the experimental criteria in this paper, FFT is proved to have a better performance to model human dynamic behavior for driver identification than PCA and ICA. Although PCA and ICA are often used for input reduction, it is not always useful because the variance of a signal is not necessarily related to the importance of the variable. Human behavior contains much signals at lower frequencies and FFT can retain the energy at this frequent area but PCA and ICA work bad as there are too many isotropically distributed clusters. By reducing the redundancy in the input data, the training process of the human driving behavior model becomes more efficient. After the unnecessary information is removed from the inputs, not only the key characteristics of the human behavior data can be retained, but also the modeling power of the SVM is actually improved.
Besides choosing preprocessing methods, the SVM model design is an important issue in this section. We have discussed the application of the multiclass SVM's classifiers and compared them with different SVM parameters to identify different drivers. The basic idea of SVM is to determine the structure of the classifier by minimizing the bounds of the training error and generalization error. The SVs close to the boundary decision surface determine the efficacy of the classifier. Based on the results from our application, SVM with polynomial kernel achieves the better performance. Our results demonstrate that the SVMs have the potential to obtain a reliable distinction among our testing human subjects, individual identification can be recognized with the multiclass SVM's classifiers with a success rate of over 85%, which verifies that the proposed SVM modeling method is valid and useful against the vehicle thefts problem.
In this paper, we focus on the research of utilizing dynamic human behavior models for vehicle security (preventing vehicles from being stolen) application. By learning from driving performances, the intelligent classifier can be embedded into an IC-based car key, through which the vehicle security system can identify the valid drivers based on the ways the vehicle are driven and the drivers behave.
We proposed the innovative idea on driver identification system for detecting vehicle theft based on dynamic human behaviors. The dynamic and stochastic feature is difficult to be handled by traditional mathematical methods. We compared FFT, PCA and ICA in the data preprocessing, and proved FFT has better performance to process human dynamic behavior. Thereafter, machine learning method based on SVM is applied. We discussed the application of the multi-class SVM classifiers and compared the performance of different SVM parameters. SVM with polynomial kernel performs better than other functions.
Choosing the best parameters, especially if a systematic approach is not used and/or the problem knowledge do not aid for proper selection, can be time consuming since we have to rely upon guessing and trial-and-error techniques. Therefore, an interactive grid search model selection method can be studied, which may further enhance the accuracy.
This work is partially supported by Hong Kong RGC CUHK417605, Hong Kong ITF ITP/003/09AP, GHP/006/09SZ, the grant from Key Laboratory of Robotics and Intelligent System, Guangdong Province (2009A060800016), the Knowledge Innovation Program of the Chinese Academy of Sciences Grant No. KGCX2-YW-156, and the grant from Shenzhen Hong Kong Innovation Circle.