Driver Identification Using Optimized Deep Learning Model in Smart Transportation

Abstract

The Intelligent Transportation System (ITS) is said to revolutionize the travel experience by making it safe, secure and comfortable for the people. Although vehicles have been automated up to a certain extent it still has critical security issues that require thorough study and advanced solutions. The security vulnerabilities of ITS allows the attacker to steal the vehicle. Therefore, the identification of drivers is required in order to develop a safe and secure system so that the vehicles can be protected from theft. There are two ways in which a driver can be identified 1) face recognition of the driver and 2) based on driving behavior. Face recognition includes image processing of 2-D images and learning of the features, which require high computational power. Drivers are known to have unique driving styles, whose data can be captured by the sensors. Therefore, the second method identifies drivers based on the analysis of the sensor data and it requires comparatively lesser computational power. In this paper, an optimized deep learning model is trained on the sensor data to correctly identify the drivers. The Long Short Term Memory (LSTM) deep learning model is optimized for better performance. The novelty of the approach in this work is the inclusion of hyperparameter tuning using a nature-inspired optimization algorithm, which is an important and essential step in discovering the optimal hyperparameters for training the model which in turn increases the accuracy. The CAN-BUS dataset is used for experimentation and evaluation of the training model. Evaluation parameters such as accuracy, precision score, F1 score and ROC AUC curve are considered to evaluate the peformance of the model.

Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review

“…RFID readers are deployed at toll booths that automatically deduct toll amounts after reading RFID tags on vehicles. In the transportation sector, smart vehicles reduce the travelling time and also fuel consumption with low cost of mobility and reduced human efforts [ 11 ], atmospheric monitoring reduces pollution, and surveillance applications reduce crime. Nowadays, WSN also plays a role in precision agriculture.…”

Driver Identification Using Statistical Features of Motor Activity and Genetic Algorithms

“…In addition, Ullah et al [ 21 ] uses the driver’s behavior data (with CAN-bus) with the objective to identify the driver of a given unseen driving data, using a lightweight deep learning model obtaining accuracy greater than 90%. Likewise, Ravi et al [ 22 ] proposed optimized deep learning using long and short memory for better training on various data collected from CAN-bus, and the evaluation metrics were calculated on the test data. The performance of the proposed model was compared with a few baseline machine learning models, obtaining an accuracy of 99%, with the proposed model having the best results.…”

“…One less explored scenario to date, where conditions can be controlled to obtain more objective results, is driver identification in simulated environments, as presented by Yang et al [ 22 ]. They developed driver identification, using deep learning architecture (Driver2vec), which transfers a brief interval of driving data into an embedding space that represents driver behavior.…”

An Electro-Oculogram (EOG) Sensor’s Ability to Detect Driver Hypovigilance Using Machine Learning

“…The uses of blockchain for big data applications in different vertical domains such as smart cities, smart healthcare, smart transportation, and smart grid were reviewed in [ 16 ]. In [ 17 ], the camera was not only used for the identification of the driver’s face recognition but also to prevent the vehicles from theft.…”