Design of a Highly Portable Data Logging Embedded System for Naturalistic Motorcycle Study

According to Motorcycle Industrial Council (MIC), in USA the number of owned motorcycle increased during last few years and most likely will keep increasing. However, the number of the deadly crash accidents associated with motorcycles is on the rise. Although MIC doesn\u27t explain why the accident rate has increased, the unprotected motorcyclist gear can be one of the reasons. The most recent National Highway Traffic Safety Administration (NHTSA) annual report stated that its data analyses are based on their experiences and the best judgment is not based on solid scientific experiment [3]. Thus, building a framework for the data acquisition about the motorcyclist environment is a first step towards decreasing motorcyclist crashes. There are a few naturalistic motorcycle studies reported in the literature. The naturalistic motorcycle study also identifies the behaviors and environmental crash hazards. The primary objective of this thesis work is to design a highly portable data logging embedded system for naturalistic motorcycle study with capability of collecting many types of data such as images, speed, acceleration, time, location, distance approximation, etc. This thesis work is the first phase (of three phases) of a naturalistic motorcycle study project. The second phase is to optimize system area, form factor, and power consumption. The third phase will be concerned with aggressive low power design and energy harvesting. The proposed embedded system design is based on an Arduino microcontroller. A whole suite of Arduino based prototype boards, sensor boards, support software, and user forum is available. The system is high portable with capability to store up to eight (8) hours of text/image data during a one month study period. We have successfully designed and implemented the system and performed three trial runs. The data acquired has been validated and found to be accurate

Building a Driving Simulator as an Electric Vehicle Hardware Development Tool

Driving simulators have been used to support the development of new vehicle systems for many years. The rise of electric vehicles (EVs) as a means of reducing carbon emissions has lead to the emergence of a number of new design challenges related to the performance of EV components and the flow of power under a variety of circumstances. In this paper we describe the integration of an EV drive train test system with a driving simulator to allow the performance of EV systems to be investigated while under the control of real drivers in simulated scenarios. Such a system offers several potential benefits. The performance of EV drive trains can be evaluated subjectively by real world users while the electrical and mechanical properties can be tested under a variety of conditions which would be difficult to replicate using standard drive cycles

Data collection, analysis methods and equipment for naturalistic studies and requirements for the different application areas. PROLOGUE Deliverable D2.1

Naturalistic driving observation is a relatively new method for studying road safety issues, a method by which one can objectively observe various driver- and accident related behaviour. Typically, participants get their own vehicles equipped with some sort of data logging device that can record various driving behaviours such as speed, braking, lane keeping/variations, acceleration, deceleration etc., as well as one or more video cameras. In this way normal drivers are observed in their normal driving context while driving their own vehicles. Optimally, this allows for observation of the driver, vehicle, road and traffic environments and interaction between these factors. The main objective of PROLOGUE is to demonstrate the usefulness, value, and feasibility of conducting naturalistic driving observation studies in a European context in order to investigate traffic safety of road users, as well as other traffic related issues such as eco-driving and traffic flow/traffic management. The current deliverable aims to develop an inventory of the current and appropriate data collection and data analysis equipment for naturalistic observation studies together with a theoretical analysis of the requirements for different application areas. The deliverable also discusses data quality issues and top level data base management requirements. Among the reviewed literature, maximal use is made of the extensive knowledge and experience that comes from the EU projects FESTA and EuroFOT, the 100car study and the SHRP2 preparatory safety

AI-based framework for automatically extracting high-low features from NDS data to understand driver behavior

Our ability to detect and characterize unsafe driving behaviors in naturalistic driving environments and associate them with road crashes will be a significant step toward developing effective crash countermeasures. Due to some limitations, researchers have not yet fully achieved the stated goal of characterizing unsafe driving behaviors. These limitations include, but are not limited to, the high cost of data collection and the manual processes required to extract information from NDS data. In light of this limitations, the primary objective of this study is to develop an artificial intelligence (AI) framework for automatically extracting high-low features from the NDS dataset to explain driver behavior using a low-cost data collection method. The author proposed three novel objectives for achieving the study's objective in light of the identified research gaps. Initially, the study develops a low-cost data acquisition system for gathering data on naturalistic driving. Second, the study develops a framework that automatically extracts high- to low-level features, such as vehicle density, turning movements, and lane changes, from the data collected by the developed data acquisition system. Thirdly, the study extracted information from the NDS data to gain a better understanding of people's car-following behavior and other driving behaviors in order to develop countermeasures for traffic safety through data collection and analysis. The first objective of this study is to develop a multifunctional smartphone application for collecting NDS data. Three major modules comprised the designed app: a front-end user interface module, a sensor module, and a backend module. The front-end, which is also the application's user interface, was created to provide a streamlined view that exposed the application's key features via a tab bar controller. This allows us to compartmentalize the application's critical components into separate views. The backend module provides computational resources that can be used to accelerate front-end query responses. Google Firebase powered the backend of the developed application. The sensor modules included CoreMotion, CoreLocation, and AVKit. CoreMotion collects motion and environmental data from the onboard hardware of iOS devices, including accelerometers, gyroscopes, pedometers, magnetometers, and barometers. In contrast, CoreLocation determines the altitude, orientation, and geographical location of a device, as well as its position relative to an adjacent iBeacon device. The AVKit finally provides a high-level interface for video content playback. To achieve objective two, we formulated the problem as both a classification and time-series segmentation problem. This is due to the fact that the majority of existing driver maneuver detection methods formulate the problem as a pure classification problem, assuming a discretized input signal with known start and end locations for each event or segment. In practice, however, vehicle telemetry data used for detecting driver maneuvers are continuous; thus, a fully automated driver maneuver detection system should incorporate solutions for both time series segmentation and classification. The five stages of our proposed methodology are as follows: 1) data preprocessing, 2) segmentation of events, 3) machine learning classification, 4) heuristics classification, and 5) frame-by-frame video annotation. The result of the study indicates that the gyroscope reading is an exceptional parameter for extracting driving events, as its accuracy was consistent across all four models developed. The study reveals that the Energy Maximization Algorithm's accuracy ranges from 56.80 percent (left lane change) to 85.20 percent (right lane change) (lane-keeping) All four models developed had comparable accuracies to studies that used similar models. The 1D-CNN model had the highest accuracy (98.99 percent), followed by the LSTM model (97.75 percent), the RF model (97.71 percent), and the SVM model (97.65 percent). To serve as a ground truth, continuous signal data was annotated. In addition, the proposed method outperformed the fixed time window approach. The study analyzed the overall pipeline's accuracy by penalizing the F1 scores of the ML models with the EMA's duration score. The pipeline's accuracy ranged between 56.8 percent and 85.0 percent overall. The ultimate goal of this study was to extract variables from naturalistic driving videos that would facilitate an understanding of driver behavior in a naturalistic driving environment. To achieve this objective, three sub-goals were established. First, we developed a framework for extracting features pertinent to comprehending the behavior of natural-environment drivers. Using the extracted features, we then analyzed the car-following behaviors of various demographic groups. Thirdly, using a machine learning algorithm, we modeled the acceleration of both the ego-vehicle and the leading vehicle. Younger drivers are more likely to be aggressive, according to the findings of this study. In addition, the study revealed that drivers tend to accelerate when the distance between them and the vehicle in front of them is substantial. Lastly, compared to younger drivers, elderly motorists maintain a significantly larger following distance. This study's results have numerous safety implications. First, the analysis of the driving behavior of different demographic groups will enable safety engineers to develop the most effective crash countermeasures by enhancing their understanding of the driving styles of different demographic groups and the causes of collisions. Second, the models developed to predict the acceleration of both the ego-vehicle and the leading vehicle will provide enough information to explain the behavior of the ego-driver.Includes bibliographical references

Modeling drivers’ naturalistic driving behavior on rural two-lane curves

This dissertation examined drivers’ naturalistic driving behavior on rural two-lane curves using the Strategic Highway Research Program 2 Naturalistic Driving Study data. It is a state-of-the-art naturalistic driving study that collected more than 3,000 drivers’ daily driving behavior over two years in the U.S. The major data sources were vehicle network, lane tracking system, front and rear radar, driver demographics, driver surveys, vehicle characteristics, and video cameras. This dissertation has three objectives: 1) examine the contributing factors to crashes and near-crashes on rural two-lane curves; 2) understand drivers’ normal driving behavior on rural two lane curves; 3) evaluate how drivers continuously interact with curve geometries using functional data analysis. The first study analyzed the crashes and near-crashes on rural two-lane curves using logistic regression model. The model was used to predict the binary event outcomes using a number of explanatory variables, including driver behavior variables, curve characteristics, and traffic environments. The odds ratio of getting involved in safety critical events was calculated for each contributing factor. Furthermore, the second study focused on the analysis of drivers’ normal curve negotiation behavior on rural two-lane curves. Significant relationships were found between curve radius, lateral acceleration, and vehicle speeds. A linear mixed model was used to predict mean speeds based on curve geometry and driver factors. The third analysis applied functional data analysis method to analyze the time series speed data on four example curves. Functional data analysis was found to be a useful method to analyze the time series observations and understand driver’s behavior from naturalistic driving study. Overall, this dissertation is one of the first studies to investigate drivers’ curve negotiation behavior using naturalistic driving study data, and greatly enhanced our understanding about the role of driver behavior in curve negotiation process. This dissertation had many important implications for curve geometry design, policy making, and advanced vehicle safety system. This dissertation also discussed the opportunities and challenges of analyzing the Strategic Highway Research Program 2 Naturalistic Driving Study data, and the implications for future research

Autonomous driving: a bird's eye view

[Abstract:] The introduction of autonomous vehicles (AV) will represent a milestone in the evolution of transportation and personal mobility. AVs are expected to significantly reduce accidents and congestion, while being economically and environmentally beneficial. However, many challenges must be overcome before reaching this ideal scenario. This study, which results from on-site visits to top research centres and a comprehensive literature review, provides an overall state-of-the-practice on the subject and identifies critical issues to succeed. For example, although most of the required technology is already available, ensuring the robustness of AVs under all boundary conditions is still a challenge. Additionally, the implementation of AVs must contribute to the environmental sustainability by promoting the usage of alternative energies and sustainable mobility patterns. Electric vehicles and sharing systems are suitable options, although both require some refinement to incentivise a broader range of customers. Other aspects could be more difficult to resolve and might even postpone the generalisation of automated driving. For instance, there is a need for cooperation and management strategies geared towards traffic efficiency. Also, for transportation and land-use planning to avoid negative territorial and economic impacts. Above all, safe and ethical behaviour rules must be agreed upon before AVs hit the road.Ministerio de Economía y Competitividad; TRA2016-79019-R/COO