The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obvious...
The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.
The development of autonomous driving and Advanced Driver Assistance System (ADAS) technology has grown rapidly in recent years. As most traffic accidents occur due to human error, self-driving vehicles can drastically reduce the number of accidents and crashes that occur on the roads today. Obviously, technical advancements in autonomous driving can lead to improved public driving safety. However, due to the current limitations in technology and lack of public trust in self-driving cars (and drones), the actual use of Autonomous Vehicles (AVs) is still significantly low. According to prior studies, people's acceptance of an AV is mainly determined by trust. It is proven that people still feel much more comfortable in personalized ADAS, designed with the way people drive. Based on such needs, a new attempt for a customized ADAS considering each driver's driving style is proposed in this paper. Each driver's behavior is divided into two categories: assertive and defensive. In this paper, a novel customized ADAS algorithm with high classification accuracy is designed, which divides each driver based on their driving style. Each driver's driving data is collected and simulated using CARLA, which is an open-source autonomous driving simulator. In addition, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) machine learning algorithms are used to optimize the ADAS parameters. The proposed scheme results in a high classification accuracy of time series driving data. Furthermore, among the vast amount of CARLA-based feature data extracted from the drivers, distinguishable driving features are collected selectively using Support Vector Machine (SVM) technology by comparing the amount of influence on the classification of the two categories. Therefore, by extracting distinguishable features and eliminating outliers using SVM, the classification accuracy is significantly improved. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support. The proposed technology of this paper is especially important because currently, the state-of-the-art level of autonomous driving is at level 3 (based on the SAE International driving automation standards), which requires advanced functions that can assist drivers using ADAS technology.
A novel simulation-based personalized ADAS algorithm with high classification accuracy is designed in this paper, which divides each driver based on their driving style. Each driver’s driving data is first collected and simulated using CARLA.
First of all, an experiment was conducted to classify driving data using LSTM and GRU, respectively, and experiments with ‘SVM+LSTM’ and ‘SVM+GRU’ models were also conducted using a trained SVM classifier
In addition, so far little research has been conducted to address this problem. In this paper, for the first time, the impact of assertive drivers and defensive drivers on each ADAS parameter is analyzed, and the derived results are used to classify them, thereby laying the foundation for autonomous vehicle technology that drivers can utilize with sufficient confidence with the development of personalized ADAS. In addition, the reliability of this algorithm was increased by using stable and reliable data, which is collected by using CARLA.
LSTM and GRU, which were described in the previous section, were used to classify drivers into two categories, assertive and defensive. Since the driving data is time-series data, which means the data highly depends on time, it is efficient to train RNN-based LSTM and GRU models and use them to make predictions.
The proposed scheme analyzes each driver’s driving behavior and preference to classify them into two groups: assertive and defensive drivers
In the proposed scheme, the driver’s driving data is generated using the CARLA simulator. The proposed scheme uses SVM to first extract the distinguishable driving features to increase the classifying performance, and then LSTM and GRU models were used to implement the classifier, based on their RNN characteristics to perform well in conducting sequential data analysis and optimized control.
Therefore, in this paper, a newly designed model is proposed, which analyzes and classifies the driving styles of each driver and recommends driving modes according to their propensity. Driving data for this personalized ADAS system was collected by using the CARLA simulator.
대상 데이터
At this time, several buildings or people appearing on the road were fixed with the same seed value, so that the experiment could be conducted in the same traffic situation. The data includes about 30 sensor-based driving features with about 8300 rows of time series.
In the actual experiment, it took about two hours to train both models, but since the structure of GRU is a little simpler than LSTM, GRU took a little shorter time to learn. The total data consists of 8300 rows with 30 features. Accordingly, it took about 867.
성능/효과
In addition, since SVM is a model that linearly solves classification or regression problems, it takes much less time to train and evaluate compared to LSTM and GRU that sequentially process time-series data. Accordingly, despite a large amount of data, the time required was less than 15 minutes. In other words, SVM took about 108.
The total data consists of 8300 rows with 30 features. Accordingly, it took about 867.46 ms to process one-row data containing 30 features and about 28.92 ms was required to train 1 feature data in one-row data. In addition, since SVM is a model that linearly solves classification or regression problems, it takes much less time to train and evaluate compared to LSTM and GRU that sequentially process time-series data.
Among these functionalities, this paper introduced a user-based self-driving mode recommendation system by classifying each driver’s tendencies based on cruise control.
However, most people are not ready to embrace self-driving technology yet. As a result, the actual use of AVs is still significantly low, as people have low confidence in self-driving cars and feel less comfortable. According to prior studies, pacceptance of an AV is mainly determined by trust, which is yet to convince people to explore and fully utilize new autonomous technologies.
As the stage of autonomous driving technology increases, the importance of the ptechnology increases, as it can enhance the safety of AVs. In addition, it is shown that advanced safety systems increase safety by reducing the overall number of traffic accidents.
First of all, an experiment was conducted to classify driving data using LSTM and GRU, respectively, and experiments with ‘SVM+LSTM’ and ‘SVM+GRU’ models were also conducted using a trained SVM classifier. At this time, in order to further increase the feature selection accuracy in the process of training the SVM, the ROC curve is used to verify the accuracy of the SVM model when selecting the top significant features that well reflect the driving preferences of each driver.
In this case, aggressive drivers are defined as assertive drivers, who prefer aggressive driving habits but try to remain in safe conditions. Based on this classification, the ADAS sensors can be made more sensitive for the case of assertive drivers, enabling more advanced driving safety support.
In addition, the classification accuracy was further improved by additionally utilizing SVM to extract and analyze features that can better reflect drivers' driving tendencies in advance. By additionally going through this process, a classification model using refined driving features is created so that it can show more stable and higher performance.
By collecting data using CARLA simulator that provides a constant driving environment, ADAS parameters that are affected by drivers' driving tendencies are correctly extracted and the influence they receive according to drivers’ driving preferences are relatively accurately measured.
The experiment was conducted using four different classifiers. First, LSTM and GRU were used to measure the classification accuracy of each driver. In addition, a feature selection step was added for the ‘SVM+LSTM’ model and the ‘SVM+GRU’ model.
The model proposed in this paper has the structure described in Figure 2. First, the driving data is collected using the CARLA simulator. A vast amount of driving data was collected from assertive drivers and defensive drivers.
After that, each sensor value is inserted as an input of the LSTM and GRU models and goes through the process of classifying them into assertive drivers and defensive drivers. Furthermore, the classification performance and accuracy were improved by additionally utilizing the SVM classifier to extract the top distinguishable features that particularly well reflect the driving tendency among the ADAS parameters. In this paper, experiments were conducted on a total of four models.
As the stage of autonomous driving technology increases, the importance of the ptechnology increases, as it can enhance the safety of AVs. In addition, it is shown that advanced safety systems increase safety by reducing the overall number of traffic accidents. Thus, the increase in the usage of AVs becomes one of the most important factors to protect public road safety, and this paper proposes a novel method to significantly improve overall performance.
92 ms was required to train 1 feature data in one-row data. In addition, since SVM is a model that linearly solves classification or regression problems, it takes much less time to train and evaluate compared to LSTM and GRU that sequentially process time-series data. Accordingly, despite a large amount of data, the time required was less than 15 minutes.
This is mainly because features that are somewhat irrelevant to driving behaviors are eliminated when training LSTM and GRU if the additional SVM step is introduced. In addition, since the experiment was conducted in a way that was driven in heavy traffic road conditions, the actual continuous time series were relatively short, resulting in higher performance on more efficient and simple GRUs than on complex LSTMs.
In addition, the classification accuracy was further improved by additionally utilizing SVM to extract and analyze features that can better reflect drivers' driving tendencies in advance
In this paper, for the first time, the impact of assertive drivers and defensive drivers on each ADAS parameter is analyzed, and the derived results are used to classify them, thereby laying the foundation for autonomous vehicle technology that drivers can utilize with sufficient confidence with the development of personalized ADAS. In addition, the reliability of this algorithm was increased by using stable and reliable data, which is collected by using CARLA.
Driving data for this personalized ADAS system was collected by using the CARLA simulator. In addition, various machine learning-based algorithms were applied to improve the accuracy and performance of the classification model.
Accordingly, despite a large amount of data, the time required was less than 15 minutes. In other words, SVM took about 108.43 ms to process one-row data containing 30 features, and 3.61 ms to learn 1 feature in one row, which is about 0.125 times longer than LSTM or GRU training and processing time.
On the other hand, GRU consists of a total of two gates, a reset gate, and an update gate, and is a slightly simplified version of the time-step cell constituting LSTM. In the actual experiment, it took about two hours to train both models, but since the structure of GRU is a little simpler than LSTM, GRU took a little shorter time to learn. The total data consists of 8300 rows with 30 features.
In this paper, a novel personalized ADAS scheme is proposed to improve the current state-of-the-art level 3 AV systems. The proposed scheme analyzes each driver’s driving behavior and preference to classify them into two groups: assertive and defensive drivers.
It is shown that the SVM scheme shows high classification accuracy. The average cross-validation score is about 0.
Figure 5 describes the convergence of classification accuracy of the four different proposed models as the epoch increases. It is shown that the overall train accuracy is high, which means that the proposed systems perform well as binary classifiers. Among these models, the ‘SVM+GRU’ combined model shows the highest accuracy of 0.
It is shown that the SVM scheme shows high classification accuracy. The average cross-validation score is about 0.9733, and the classification accuracy of the SVM model is about 0.9734, which is very close to 1.
The biggest challenge is that people’s trust or acceptance of AVs is low, so the actual AV usage is also significantly low
2. The proposed algorithm used LSTM and GRU for time-series driving data to classify drivers according to their driving tendencies with high accuracy. In addition, the classification accuracy was further improved by additionally utilizing SVM to extract and analyze features that can better reflect drivers' driving tendencies in advance.
Therefore, in this paper, a new attempt at a personalized ADAS is proposed, by designing a personalized AV algorithm based on each driver’s driving style
In general, models combined with SVM show a higher performance compared to the models that don’t include the feature extraction step supported by SVM. This is mainly because features that are somewhat irrelevant to driving behaviors are eliminated when training LSTM and GRU if the additional SVM step is introduced. In addition, since the experiment was conducted in a way that was driven in heavy traffic road conditions, the actual continuous time series were relatively short, resulting in higher performance on more efficient and simple GRUs than on complex LSTMs.
Weighted Average Precision: Weighted Average Precision is used by giving weight to the class that should be emphasized when the number or ratio of each class is different.
후속연구
Therefore, it is important to pay attention to the sensors and parameters that include the above-mentioned data, which best represent drivers’ driving tendencies. By doing this, it is possible to approach personalized ADAS technology that can provide a higher reliability to drivers.
In order to increase people’s reliability in self-driving cars, it is necessary to introduce more advanced ADAS technology designed according to each person’s driving style.
The biggest challenge is that people’s trust or acceptance of AVs is low, so the actual AV usage is also significantly low. Therefore, this paper proposes a novel method to classify each driver considering their driving behaviors so the drivers who utilize this technology would feel much more comfortable and have more trust in personalized ADAS driving performance and safety. In the proposed scheme, the driver’s driving data is generated using the CARLA simulator.
In addition, it is shown that advanced safety systems increase safety by reducing the overall number of traffic accidents. Thus, the increase in the usage of AVs becomes one of the most important factors to protect public road safety, and this paper proposes a novel method to significantly improve overall performance.
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