Kumar Raman, Jain Anuj
Lovely Professional University, Phagwara, India.
J Supercomput. 2023 May 19:1-20. doi: 10.1007/s11227-023-05364-3.
The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.
运输行业对提高性能和降低成本的关注推动了物联网和机器学习技术的整合。驾驶风格和行为与燃油消耗及排放之间的关联凸显了对不同驾驶员驾驶模式进行分类的必要性。作为回应，车辆现在配备了能够收集广泛运行数据的传感器。所提出的技术通过车载诊断接口收集关键的车辆性能数据，包括速度、发动机每分钟转速、踏板位置、确定的发动机负载以及其他50多个参数。车载诊断第二代（OBD-II）诊断协议是技术人员使用的主要诊断流程，可通过汽车的通信端口获取这些信息。OBD-II协议用于获取与车辆运行相关的实时数据。这些数据用于收集与发动机运行相关的特性并协助进行故障检测。所提出的方法使用机器学习技术，如支持向量机（SVM）、自适应增强（AdaBoost）和随机森林，基于包括燃油消耗、转向稳定性、速度稳定性和制动模式在内的十个类别对驾驶员的行为进行分类。该解决方案提供了一种有效的手段来研究驾驶行为，并为高效安全驾驶推荐纠正措施。所提出的模型基于燃油消耗、转向稳定性、速度稳定性和制动模式对十种驾驶员类别进行分类。这项研究工作使用通过OBD-II协议从发动机内部传感器提取的数据，无需额外的传感器。收集到的数据用于构建一个对驾驶员行为进行分类的模型，并可用于提供反馈以改善驾驶习惯。关键驾驶事件，如高速制动、快速加速、减速和转弯，用于刻画个体驾驶员。可视化技术，如折线图和相关矩阵，用于比较驾驶员的表现。模型中考虑了传感器数据的时间序列值。采用监督学习方法对所有驾驶员类别进行比较。支持向量机、自适应增强和随机森林算法的准确率分别为99%、99%和100%。所建议的模型提供了一种实用的方法来检查驾驶行为，并提出提高驾驶安全性和效率的必要措施。
