A Driver Behavior Modeling Structure Based on Non-parametric Bayesian Stochastic Hybrid Architecture

Heterogeneous nature of the vehicular networks, which results from the co-existence of human-driven, semi-automated, and fully autonomous vehicles, is a challenging phenomenon toward the realization of the intelligent transportation systems with an acceptable level of safety, comfort, and efficiency. Safety applications highly suffer from communication resource limitations, specifically in dense and congested vehicular networks. The idea of model-based communication (MBC) has been recently proposed to address this issue. In this work, we propose Gaussian Process-based Stochastic Hybrid System with Cumulative Relevant History (CRH-GP-SHS) framework, which is a hierarchical stochastic hybrid modeling structure, built upon a non-parametric Bayesian inference method, i.e. Gaussian processes. This framework is proposed in order to be employed within the MBC context to jointly model driver/vehicle behavior as a stochastic object. Non-parametric Bayesian methods relieve the limitations imposed by non-evolutionary model structures and enable the proposed framework to properly capture different stochastic behaviors. The performance of the proposed CRH-GP-SHS framework at the inter-mode level has been evaluated over a set of realistic lane change maneuvers from NGSIM-US101 dataset. The results show a noticeable performance improvement for GP in comparison to the baseline constant speed model, specifically in critical situations such as highly congested networks. Moreover, an augmented model has also been proposed which is a composition of GP and constant speed models and capable of capturing the driver behavior under various network reliability conditions.Comment: This work has been accepted in 2018 IEEE Connected and Automated Vehicles Symposium (CAVS 2018

Development of collision avoidance application using internet of things (loT) technology for vehicle-to-vehicle (v2v) and vehicle-to-infrastructure (v21) communication system

Rising number of road accidents have been a common issue that needs to be given attention where most of it causes fatal injury and death. 30% of accidents are involving rear-to-end crashes meanwhile more than 900,000 cases in a year are related to rearblind-spots. Even though safety improvements have been upgraded such as introduction of Assistance Driving Assistance System (ADAS), yet the numbers are still on its endangering path. To solve this issue, Vehicle Ad-hoc Networks (VANET) system is invented to ensure a safer environment for drivers and pedestrians. Vehicle-toInfrastructure (V2I) and Vehicle-to-Vehicle (V2V) Communication System is one of the technologies created under VANET . This dissertation presented the new V2V and V2I system that is applicable to avoid collisions with development of On-Board Unit (OBU) and Roadside Unit (RSU) prototype using Internet of Things (IoT) technology. Single-Board Computers (SBC) is integrated with sensors such as GPS, LiDAR and ultrasonic for OBU while DHT22, CO gas sensor, PM sensor and rain sensor for RSU. Both OBU and RSU connected to internet via 4G module integrated on the SBC which also function as Apache-MySQL-PHP (AMP) server. Location Tracker, Forward Collision Warning (FCW) and Blind Spot Warning (BSW) application is embedded into OBU located in a vehicle known as a Subject Vehicle (SV). All testing involved with obstacle vehicle known as Host Vehicle (HV) executed at Universiti Malaysia Pahang (UMP) Pekan campus. Finding shows that OBU‟s location is as accurate as 0.0124% in latitude while 0.0084% in longitude in real-time at 60 km/h. Such GPS accuracy allow FCW application to generate alert at CP of 80% to the driver. FCW developed is tested at different speed of SV and HV and findings shows that alert is generated at a safe distance and sufficient time for the driver to react. Throughout the field testing, the new TTC has been successfully formulated and verified where the real time distance has been subtracted to 1 meter over current speed. Collision percentage (CP) of 80% is still generated even though the average lagging time (LT) delay of SBC is recorded at 1.3 seconds. The new formulated TTC and CP proven that the driver has ample time to respond to the generated alert, e.g., for the case of HV is at 0 km/h and SV is at 60 km/h, alert is generated at CP of 84.04% with TTC recorded at 2.4s, which is almost aligned with recommendation of International Organizations of Standardization 2013 stating 2.6s is the best time for driver to react. Even though there was a slight delay with the alerts, with consideration of 1m safe distance and 1.3s LT, driver was able to pull off a safe braking after the alert to slow down SV thus to avoid collision from happening. For BSW application, promising results by having 1 second delay in detecting blinded HV at the constant span of 40km/h speed limit between SV and HV which is an enabler to the safe lane changing operation. The presence of host vehicle (HV) or any obstacles is detected in the blinded area of SV. In contrast to OBU, RSU is developed to monitor the weather which in turn influenced the road conditions and eventually lead to the traffic status monitoring. The RSU‟s sensors are sensitively detected the haze, rain, temperature and humidity accurately. Therefore, this system is potentially to produce Variable Speed Limit (VSL) based on the environment conditions. Speed Limit information from the RSU can be accessed through the OBU inside the vehicles using internet from the 4G technology. Implementation of IoT technology has proven to assist the drivers in avoiding collisions thuspotential to reduce the road accidents

A Simulation Framework for Traffic Safety with Connected Vehicles and V2X Technologies

With the advancement in automobile technologies, existing research shows that connected vehicle (CV) technologies can provide better traffic safety through Surrogate Safety Measure (SSM). CV technologies involves two network systems: traffic network and wireless communication network. We found that the research in the wireless communication network for CV did not interact properly with the research in SSM in transportation network, and vice versa. Though various SSM has been proposed in previous studies, a few of them have been tested in simulation software in limited extent. On the other hand, A large body of researchers proposed various communication architecture for CV technologies to improve communication performance. However, none of them tested the advanced SSM in their proposed architecture. Hence, there exists a research gap between these two communities, possibly due to difference in research domain. In this study, we developed a V2X simulation framework using SUMO, OMNeT++ and Veins for the development and testing of various SSM algorithms in run time simulation. Our developed framework has three level of communication ( CV to RSU To TS) system and is applicable for large traffic network that can have mixed traffic system (CV and non-CV), multiple road side unit (RSUs), and traffic server (TS). Moreover, the framework can be used to test SSM algorithms for other traffic networks without doing much modification. Our developed framework will be publicly available for its further development and optimization

An Adaptive Forward Collision Warning Framework Design Based on Driver Distraction

Forward Collision Warning (FCW) is a promising Advanced Driver Assistance System (ADAS) to mitigate rear-end collisions. The deterministic FCW approaches may occasionally lead to the issuance of annoying false warnings, as they cannot be individualized for different drivers. This application oversight, which may cause the driver to deactivate the system, has been tackled with some adaptive methods. However, driver distraction, which is one of the most influential driver-specific factors on FCW warnings acceptability, has not been considered yet and is analyzed in this paper for the first time. Specifically, the adaptive FCW method proposed in this paper generates the warnings by continuously comparing Time Headway with a flexible threshold. The core of the proposed threshold updating mechanism is a real-time monitoring of the driver reactions against the previously generated warnings using the available indicators such as braking history. This method considers the driver distraction in parallel to fine-tune the calculated threshold in accordance with driver cognitive state. In order to incorporate the driver distraction in the system framework, a learning-based approach is designed which continuously estimates driver distraction by the virtue of different available Controller Area Network (CAN) bus time series, such as throttle pedal position, velocity, acceleration, and yaw rate. Neural network, as a widely adopted classification method, is nominated to detect driver distraction. The framework performance is evaluated over two realistic driving datasets. An approximately 80% false warning reduction is observed in analyzed safe scenarios, while no critical warning is missed in the dangerous ones

An Adaptive Forward Collision Warning Framework Design Based On Driver Distraction

Forward Collision Warning (FCW) is a promising Advanced Driver Assistance System (ADAS) to mitigate rear-end collisions. The deterministic FCW approaches may occasionally lead to the issuance of annoying false warnings, as they cannot be individualized for different drivers. This application oversight, which may cause the driver to deactivate the system, has been tackled with some adaptive methods. However, driver distraction, which is one of the most influential driver-specific factors on FCW warnings acceptability, has not been considered yet and is analyzed in this paper for the first time. Specifically, the adaptive FCW method proposed in this paper generates the warnings by continuously comparing Time Headway with a flexible threshold. The core of the proposed threshold updating mechanism is a real-time monitoring of the driver reactions against the previously generated warnings using the available indicators such as braking history. This method considers the driver distraction in parallel to fine-tune the calculated threshold in accordance with driver cognitive state. In order to incorporate the driver distraction in the system framework, a learning-based approach is designed which continuously estimates driver distraction by the virtue of different available Controller Area Network (CAN) bus time series, such as throttle pedal position, velocity, acceleration, and yaw rate. Neural network, as a widely adopted classification method, is nominated to detect driver distraction. The framework performance is evaluated over two realistic driving datasets. An approximately 80% false warning reduction is observed in analyzed safe scenarios, while no critical warning is missed in the dangerous ones

Detection of Driver Cognitive Distraction Using Machine Learning Methods

Autonomous vehicles seem to be closer than expected on their timeline. However, there is still a decade of driving manual as well as semi-autonomous vehicles before we can experience completely automated vehicles on the road. Hence, the number of deaths due to driving accidents will take a while to drop, and we require alternative ways to prevent them. Driver distraction is one of the primary causes of accidents. Driver distraction has posed a significant problem since the first car appeared on our roadways. According to WHO findings, 1.25 million people lose their lives every year due to road traffic crashes. One of the major causes of traffic crashes is distracted driving. As a result, there is a profound need and necessity to continuously observe driver state and provide appropriately informed alerts to distracted drivers. As defined by the National Highway Traffic Safety Administration (NHTSA), there are several types of distractions including cognitive, visual and manual distractions, which may be distinguished from each other based upon the resources required to perform the task. Cognitive distraction refers to the "look but not see" situations when the drivers' eyes are focused on the forward roadway, but his/her mind is not. Typically, cognitive distractions can result from fatigue, conversation with a co-passenger, listening to the radio, or other similarly loading secondary tasks that do not necessarily take a driver's eyes off the roadway. This makes it one of the hardest distractions to detect as there are no visible clues whether the driver is distracted. In this thesis, we have identified features from different sources such as pupil size, heart rate, acceleration that are relevant to classify distracted and non-distracted drivers through collection and analysis of driving data collected from participants over multiple driving scenarios. The Machine Learning methods used dealt with classification including, but not limited to Random Forest, Decision Trees, and SVM. A reduced feature set including pupil area, pupil vertical and horizontal motion was found while maintaining an average accuracy of 90\% across different road types. Also, the impact of road types on driver behaviour is identified. Information about dominant features which affect the classification would aid early detection of distracted driving, and mitigation through the development of effective warning systems. The algorithm could be personalized to the specific driver depending on their reaction to driving situations. It would enable a safer and more comfortable driving experience

Modeling driver distraction mechanism and its safety impact in automated vehicle environment.

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly pre-defined distraction as a binary outcome and investigated the patterns that contribute to distraction from multiple perspectives. However, the magnitude of AV distraction is not accurately quantified. Moreover, past studies in quantifying distraction have mainly used wearable sensors’ data. In reality, it is not realistic for drivers to wear these sensors whenever they drive. Hence, a research motivation is to develop a surrogate model that can replace the wearable device-based data to predict AV distraction. From the safety perspective, there lacks a comprehensive understanding of how AV distraction impacts safety. Furthermore, a solution is needed for safely offsetting the impact of distracted driving. In this context, this research aims to (1) improve the existing methods in quantifying Human-Vehicle Interaction-induced (HVI-induced) driver distraction under automated driving; (2) develop a surrogate driver distraction prediction model without using wearable sensor data; (3) quantitatively reveal the dynamic nature of safety benefits and collision hazards of HVI-induced visual and cognitive distractions under automated driving by mathematically formulating the interrelationships among contributing factors; and (4) propose a conceptual prototype of an AI-driven, Ultra-advanced Collision Avoidance System (AUCAS-L3) targeting HVI-induced driver distraction under automated driving without eye-tracking and video-recording. Fixation and pupil dilation data from the eye tracking device are used to model driver distraction, focusing on visual and cognitive distraction, respectively. In order to validate the proposed methods for measuring and modeling driver distraction, a data collection was conducted by inviting drivers to try out automated driving under Level 3 automation on a simulator. Each driver went through a jaywalker scenario twice, receiving a takeover request under two types of HVI, namely “visual only” and “visual and audible”. Each driver was required to wear an eye-tracker so that the fixation and pupil dilation data could be collected when driving, along with driving performance data being recorded by the simulator. In addition, drivers’ demographical information was collected by a pre-experiment survey. As a result, the magnitude of visual and cognitive distraction was quantified, exploring the dynamic changes over time. Drivers are more concentrated and maintain a higher level of takeover readiness under the “visual and audible” warning, compared to “visual only” warning. The change of visual distraction was mathematically formulated as a function of time. In addition, the change of visual distraction magnitude over time is explained from the driving psychology perspective. Moreover, the visual distraction was also measured by direction in this research, and hotspots of visual distraction were identified with regard to driving safety. When discussing the cognitive distraction magnitude, the driver’s age was identified as a contributing factor. HVI warning type contributes to the significant difference in cognitive distraction acceleration rate. After drivers reach the maximum visual distraction, cognitive distraction tends to increase continuously. Also, this research contributes to quantitatively revealing how visual and cognitive distraction impacts the collision hazards, respectively. Moreover, this research contributes to the literature by developing deep learning-based models in predicting a driver’s visual and cognitive distraction intensity, focusing on demographics, HVI warning types, and driving performance. As a solution to safety issues caused by driver distraction, the AUCAS-L3 has been proposed. The AUCAS-L3 is validated with high accuracies in predicting (a) whether a driver is distracted and does not perform takeover actions and (b) whether crashes happen or not if taken over. After predicting the presence of driver distraction or a crash, AUCAS-L3 automatically applies the brake pedal for drivers as effective and efficient protection to driver distraction under automated driving. And finally, a conceptual prototype in predicting AV distraction and traffic conflict was proposed, which can predict the collision hazards in advance of 0.82 seconds on average

Deep Learning-based Driver Behavior Modeling and Analysis

Driving safety continues receiving widespread attention from car designers, safety regulators, and automotive research community as driving accidents due to driver distraction or fatigue have increased drastically over the years. In the past decades, there has been a remarkable push towards designing and developing new driver assistance systems with much better recognition and prediction capabilities. Equipped with various sensory systems, these Advanced Driver Assistance Systems (ADAS) are able to accurately perceive information on road conditions, predict traffic situations, estimate driving risks, and provide drivers with imminent warnings and visual assistance. In this thesis, we focus on two main aspects of driver behavior modeling in the design of new generation of ADAS. We first aim at improving the generalization ability of driver distraction recognition systems to diverse driving scenarios using the latest tools of machine learning and connectionist modeling, namely deep learning. To this end, we collect a large dataset of images on various driving situations of drivers from the Internet. Then we introduce Generative Adversarial Networks (GANs) as a data augmentation tool to enhance detection accuracy. A novel driver monitoring system is also introduced. This monitoring system combines multi-information resources, including a driver distraction recognition system, to assess the danger levels of driving situations. Moreover, this thesis proposes a multi-modal system for distraction recognition under various lighting conditions and presents a new Convolutional Neural Network (CNN) architecture, which can operate real-time on a resources-limited computational platform. The new CNN is built upon a novel network bottleneck of Depthwise Separable Convolution layers. The second part of this thesis focuses on driver maneuver prediction, which infers the direction a driver will turn to before a green traffic light is on and predicts accurately whether or not he/she will change the current driving lane. Here, a new method to label driving maneuver records is proposed, by which driving feature sequences for the training of prediction systems are more closely related to their labels. To this end, a new prediction system, which is based on Quasi-Recurrent Neural Networks, is introduced. In addition, and as an application of maneuver prediction, a novel driving proficiency assessment method is proposed. This method exploits the generalization abilities of different maneuver prediction systems to estimate drivers' driving abilities, and it demonstrates several advantages against existing assessment methods. In conjunction with the theoretical contribution, a series of comprehensive experiments are conducted, and the proposed methods are assessed against state-of-the-art works. The analysis of experimental results shows the improvement of results as compared with existing techniques