A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Computational Modeling and Experimental Research on Touchscreen Gestures, Audio/Speech Interaction, and Driving

As humans are exposed to rapidly evolving complex systems, there are growing needs for humans and systems to use multiple communication modalities such as auditory, vocal (or speech), gesture, or visual channels; thus, it is important to evaluate multimodal human-machine interactions in multitasking conditions so as to improve human performance and safety. However, traditional methods of evaluating human performance and safety rely on experimental settings using human subjects which require costly and time-consuming efforts to conduct. To minimize the limitations from the use of traditional usability tests, digital human models are often developed and used, and they also help us better understand underlying human mental processes to effectively improve safety and avoid mental overload. In this regard, I have combined computational cognitive modeling and experimental methods to study mental processes and identify differences in human performance/workload in various conditions, through this dissertation research. The computational cognitive models were implemented by extending the Queuing Network-Model Human Processor (QN-MHP) Architecture that enables simulation of human multi-task behaviors and multimodal interactions in human-machine systems. Three experiments were conducted to investigate human behaviors in multimodal and multitasking scenarios, combining the following three specific research aims that are to understand: (1) how humans use their finger movements to input information on touchscreen devices (i.e., touchscreen gestures), (2) how humans use auditory/vocal signals to interact with the machines (i.e., audio/speech interaction), and (3) how humans drive vehicles (i.e., driving controls). Future research applications of computational modeling and experimental research are also discussed. Scientifically, the results of this dissertation research make significant contributions to our better understanding of the nature of touchscreen gestures, audio/speech interaction, and driving controls in human-machine systems and whether they benefit or jeopardize human performance and safety in the multimodal and concurrent task environments. Moreover, in contrast to the previous models for multitasking scenarios mainly focusing on the visual processes, this study develops quantitative models of the combined effects of auditory, tactile, and visual factors on multitasking performance. From the practical impact perspective, the modeling work conducted in this research may help multimodal interface designers minimize the limitations of traditional usability tests and make quick design comparisons, less constrained by other time-consuming factors, such as developing prototypes and running human subjects. Furthermore, the research conducted in this dissertation may help identify which elements in the multimodal and multitasking scenarios increase workload and completion time, which can be used to reduce the number of accidents and injuries caused by distraction.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143903/1/heejinj_1.pd

Driver lane change intention inference for intelligent vehicles: framework, survey, and challenges

Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted

Time Series Categorization of Driving Maneuvers Using Acceleration Signals

Two methods of time series analysis were applied to naturalistic driving data. The SAX method reduces the dimensionality of the data by discretizing and quantizing it into distinct symbols. The matrix profile method works on raw data and computes a Euclidian distance measure between subsequences of the time series. Both methods can be used to search for motifs and discords (anomalies) in the data. We discuss the applications of these methods to look for driving patterns and show an example of a left turn that was identified using both methods. After comparing the methods, the matrix profile was the preferred method

Vision on Wheels: Looking at Driver, Vehicle, and Surround for On-Road Maneuver Analysis

Automotive systems provide a unique opportunity for mobile vision technologies to improve road safety by un-derstanding and monitoring the driver. In this work, we propose a real-time framework for early detection of driver maneuvers. The implications of this study would allow for better behavior prediction, and therefore the development of more efficient advanced driver assistance and warning systems. Cues are extracted from an array of sensors ob-serving the driver (head, hand, and foot), the environment (lane and surrounding vehicles), and the ego-vehicle state (speed, steering angle, etc.). Evaluation is performed on a real-world dataset with overtaking maneuvers, showing promising results. In order to gain better insight into the processes that characterize driver behavior, temporally dis-criminative cues are studied and visualized. 1