Human factor based advanced driver-assistance system (ADAS) design for electric vehicle - Doğan, Dağhan (Graduate School, 2022)
Every year, thousands of traffic accidents occur and thousands of people die or are injured in these accidents. Considering the causes of accidents, it can be said that most of them are human errors. For this reason, studies focus on advanced driver assistance systems, increasing vehicle autonomy levels and driver behavior in traffic, and aim to prevent possible accidents. For a similar purpose, in this study, I aimed to collect data and analyze some human factor technologies that will support advanced driver assistance systems (ADAS) and to produce suggestions on how researchers and manufacturers producing ADAS can use these technologies. Our study focuses on the data of the galvanic skin response (GSR) sensor, which is a wearable sensor and aims to contribute to human factor studies by analyzing the GSR sensor and other sensor data collected from the drivers and the prototype electric vehicle. The study is experimental and requires a realistic vehicle and realistic driver data. Thus, first of all, we aim to design a novel, low-cost and open to development embedded data collection system for the research and education in human factor technologies and ADASs. Equipment used in this simultaneous data acquisition system: an electric vehicle with the power of 750W, Arduino Mega 2560 electronic card, a 10-turn Vishay 860 potentiometer used for steering angle data, the Tamura 300 A AC/DC hall-effect current sensor used for current (torque) data, Pololu force-sensing resistor (FSR) to detect force on the steering wheel and brake pedal, Seeedstudio GSR sensor to detect stress, MinIMU-9 v3 inertial measurement unit (IMU) to detect gyro, accelerometer, and compass, GY-NEO6MV2 global positioning system (GPS) to detect chassis velocity and position, Scancon 2RM 200 encoder to detect wheel velocity and Techsmart dashcam to record the environment and driver behavior. After designing the data collection system and implementing it for the prototype electric vehicle, we are looking for an answer to the question of whether the driver's stress in traffic can be detected with the GSR and FSR sensor data. We collect the GSR and FSR sensor data for 38 drivers using the designed data collection system in the Istanbul Technical University campus and analyze the GSR and FSR sensor data. In addition, a post-driving stress survey is used to improve the reliability and consistency of the stress level analysis and to validate the results. According to analysis results, the GSR sensor detects stress level-gender, stress level-driving experience, stress level-driving frequency and stress level-representative of normal driving behavior relationship, and the FSR sensor determines only gender stress level. Here, stress level-gender results for the GSR and FSR sensor, stress level-driving experience results for the GSR sensor and stress level-driving frequency results for the GSR sensor are consistent with the results of the survey with an accuracy of 100 %. Stress level-representative of normal driving behavior results for GSR sensor are consistent with the results of the survey with an accuracy of 50 %. As a result, the GSR sensor stress results are consistent with the results of the survey with a total accuracy of 87.5 %. The FSR sensor gender stress results are consistent with the results of the survey with an accuracy of 100 %. After the stress level detection study, we collect the IMU, FSR, GSR, current sensor, potentiometer, encoder and GPS data from 38 drivers along a route. Drivers are divided into 2 (risky and normal) classes according to their Euclidean distance from expert driver data for each sensor. The best classification methods are determined along this way for each sensor. Accordingly, all data are classified with the highest accuracy of 92.1% using the Medium Gaussian Support Vector Machine (SVM) method. IMU data is classified with the highest accuracy of 89.5% using the Artificial Neural Network (ANN) method. FSR data is classified with the highest accuracy with 94.7% accuracy using the Medium Gaussian SVM method. GSR data is classified with the highest accuracy with 97.4% accuracy using the Fine K-nearest Neighbors (KNN) method. Current data is classified with the highest accuracy with 100% accuracy using the ANN method. Potentiometer data are classified with the highest accuracy of 97.3% using the ANN method. Encoder data is classified with the highest accuracy with 92.1% accuracy using the Medium Gaussian SVM method. GPS chassis velocity data is classified with the highest accuracy with 94.7% accuracy using the Medium Gaussian SVM method. Thus, we can say that driver behavior is highly predictable for the batch data along a road. Secondly, it is tried to reveal whether the driver behavior we obtained along the above road can be detected instantly. GSR data of the drivers is analyzed individually because the GSR sensor gives the driver instant excitement and stress information. The driving videos of the drivers are shown to the expert driver. The faults and fault moments of the drivers are labeled by the expert driver. On the other hand, the data obtained by the GSR sensor are used to determine when the drivers are excited (stressed) and the reasons for stress are identified by the expert driver. In the analysis, driver-4 (male) and driver-7 (female) data are examined for individual classification. Stress moments are considered class-2 as dangerous situations. Others are considered class-1. In this way, classification methods are applied. As a result, it is found that the fault moments of the drivers are a subset of the stressful moments of the drivers for all drivers. For driver-4, all sensor data which is tagged by stress moments are classified with the highest accuracy with 97.4% accuracy using the ANN method. For driver-7, all sensor data is classified with the highest accuracy with 98.6% accuracy using the Bagged Tree method. Thirdly, we validate the driver status/behavior analysis above by detecting anomalies using Local Outlier Factor (LOF) values for GSR sensor data as a different method. This analysis provides the detection of a driver status with LOF anomaly values of GSR sensor data and other sensor support (camera and GPS) without the need for machine learning. Lastly in the chapter, we analyze the driving confidence in turns. The excitement increases obtained from the GSR sensor on turns have defined the unconfidence of the driver. The velocity and current data of the drivers determined by the GSR sensor in turns are examined and thus drivers are analyzed individually. When the first junction maneuvering data of drivers are analyzed based on the GSR sensor data, drivers numbered 7, 9, 20, 23, 27 and 34 fail at authorizing driving confidence on the first turn. When the second turn data are analyzed, drivers with an ID 9, 20, 23 and 38 could not show a confident drive on the second turn. Skin conductivity information, including abnormal, risky, and unconfident driving information, can be used for torque control of an autonomous electric vehicle. We transform the semi-autonomous electric vehicle into an electric vehicle with longitudinal autonomy. To improve the study, the distance sensor is also used simultaneously with the GSR sensor to detect collisions and intervene. It means that the GSR data is used to control a vehicle with closed-loop and longitudinal autonomy, depending on the driver's condition. Stress data of 38 drivers along a road are obtained above and averaged. These averages are used as input to the system via radio frequency identification (RFID) cards. Thus, an autonomous vehicle with GSR sensor-based torque control is designed. The purpose of this transformation in this study is to integrate our work into autonomous vehicles as well as semi-autonomous vehicles. This shows that biosensors can also be used as input for autonomous vehicles. In the takeover request (TOR) study, different TOR times are tested on five different driving cases with 18 participating drivers. Three of these cases are used to detect the TOR time of drivers, while the other two scenarios are used sequentially to increase the effects of other participating vehicles such as a vehicle approaching the intersection and then is stopped on a lane along the test route causing a possible hazard situation. According to the analysis, drivers do not prefer the authority transition that is very close to the critical situation (TOR 6 s). Because in TOR 6 s, due to the approaching the critical situation, the (g) (pulse deviation) and (fxa) (current deviation multiplied by the average of five consecutive acceleration or deceleration values during manual driving) values are higher and a smooth transition does not occur. It has been observed that most of the drivers make the comfortable and smooth authority transition for TOR 4 s and TOR 2 s. The experienced drivers prefer TOR 4 for authority transition. Since the TOR 0 s authority transition is also a sudden transition, the driver is unready and causes a higher (g) and (fxa). In other words, even if the drivers are far from the critical situation, they do not prefer a sudden authority transition. Take-over request time is evaluated for each driver and three driver categories such as experienced, semi-experienced and inexperienced, and validated by a questionnaire. The TOR time is extracted and personalized for each driver, which may improve the current conditional automated driving technologies' penetration and acceptance. As the experience of the driver increases, more stable results are obtained. The TOR time for inexperienced drivers varies for each case. As a result of data analysis, the wearable biosensor GSR sensor data can be used in different human factor technologies to support ADAS. Because, as seen in our study, we detected the stress and status of the driver using the GSR sensor, detected the driver's fault cluster in traffic and trained it with machine learning methods, transformed a semi-autonomous vehicle into a GSR-based torque-controlled vehicle with longitudinal autonomy, and finally, evaluated takeover request performance using the GSR sensor.
Elektrikli araç için insan faktörü tabanlı gelişmiş sürücü yardım sistemi (ADAS) tasarımı
Thesis(Ph.D.) -- Istanbul Technical University, Graduate School, 2022
Keywords: electric vehicles; elektrikli araçlar; mechatronics; mekatronik; driver behaviors; sürücü davranışları; human factor; insan faktörü