Driving Rhythm Method for Driving Comfort Analysis on Rural Highways

Driving comfort is of great significance for rural highways, since the variation characteristics of driving speed are comparatively complex on rural highways. Earlier studies about driving comfort were usually based on the actual geometric road alignments and automobiles, without considering the driver’s visual perception. However, some scholars have shown that there is a discrepancy between actual and perceived geometric alignments, especially on rural highways. Moreover, few studies focus on rural highways. Therefore, in this paper the driver’s visual lane model was established based on the Catmull-Rom spline, in order to describe the driver’s visual perception of rural highways. The real vehicle experiment was conducted on 100 km rural highways in Tibet. The driving rhythm was presented to signify the information during the driving process. Shape parameters of the driver’s visual lane model were chosen as input variables to predict the driving rhythm by BP neural network. Wavelet transform was used to explore which part of the driving rhythm is related to the driving comfort. Then the probabilities of good, fair and bad driving comfort can be calculated by wavelets of the driving rhythm. This work not only provides a new perspective into driving comfort analysis and quantifies the driver’s visual perception, but also pays attention to the unique characteristics of rural highways.</p

Research Synthesis for the California Zero Traffic Fatalities Task Force

This research synthesis consists of a set of white papers that jointly provide a review of research on the current practicefor setting speed limits and future opportunities to improve roadway safety. This synthesis was developed to inform thework of the Zero Traffic Fatalities Task Force, which was formed in 2019 by the California State Transportation Agencyin response to California Assembly Bill 2363 (Friedman). The statutory goal of the Task Force is to develop a structured,coordinated process for early engagement of all parties to develop policies to reduce traffic fatalities to zero. Thisreport addresses the following critical issues related to the work of the Task Force: (i) the relationship between trafficspeed and safety; (ii) lack of empirical justification for continuing to use the 85th percentile rule; (iii) why we need toreconsider current speed limit setting practices; (iv) promising alternatives to current methods of setting speed limits;and (v) improving road designs to increase road user safety

Toward a Safer Transportation System for Senior Road Users

Senior pedestrians and drivers (65 years and older) are among the most vulnerable road users. As the population of seniors rise, concerns regarding older adults\u27 traffic safety are growing. The advantages of using autonomous vehicles, innovative vehicle technologies, and active transportation are becoming more widely recognized to improve seniors\u27 mobility and safety. This behooves researchers to further investigate senior road users’ safety challenges and countermeasures. This study contributes to the literature by achieving two main goals. First, to explore contributing factors affecting the safety of older pedestrians and drivers in the current transportation system. Second, to examine seniors’ perceptions, preferences, and behaviors toward autonomous vehicles and advanced vehicle technologies, the main components of future transportation systems. To achieve the first objective, crash data involving senior pedestrians and drivers were collected and analyzed. Using structural equation modeling, it was found out that seniors’ susceptibility to pedestrian incidents is a function of level of walking difficulty, fear of falling, and crossing evaluation capability. Senior drivers’ risk factors were found to be driving maneuver & crash location, road features & traffic control devices, driver condition & behavior, road geometric characteristics, crash time and lighting, road class latent factors, as well as pandemic variable. To achieve the second objective, a national survey and a driving simulator experiment were conducted among seniors. The national survey investigates seniors’ perceptions and attitudes to a wide range of AVs features from the perspective of pedestrians and users. Using principal component analysis and cluster analysis, three distinctive clusters of seniors were identified with different perceptions and attitude toward different AV options. The driving simulator experiment examined drivers’ behavior and preferences towards vehicle to infrastructure warning messages. Using the analysis of covariance technique, the results revealed that audio warning message was more effective compared to other scenarios. This finding is consistent with the results of stated preferences of the participants. Female and senior drivers had higher speed limit compliance rate. The findings of this study shed light on key aspects of the current and future of transportation systems that are needed to improve the safety of senior road users

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

Driver glance behaviors and scanning patterns: Applying static and dynamic glance measures to the analysis of curve driving with secondary tasks

Performing secondary tasks (or non‐driving‐related tasks) while driving on curved roads may be risky and unsafe. The purpose of this study was to explore whether driving safety in situations involving curved roads and secondary tasks can be evaluated using multiple measures of eye movement. We adopted Markov‐based transition algorithms (i.e., transition/stationary probabilities, entropy) to quantify drivers’ dynamic eye movement patterns, in addition to typical static visual measures, such as frequency and duration of glances. The algorithms were evaluated with data from an experiment (Jeong & Liu, 2019) involving multiple road curvatures and stimulus‐response secondary task types. Drivers were more likely to scan only a few areas of interest with a long duration in sharper curves. Total head‐down glance time was longer in less sharp curves in the experiment, but the probability of head‐down glances was higher in sharper curves over the long run. The number of reliable transitions between areas of interest varied with the secondary task type. The visual scanning patterns for visually undemanding tasks were as random as those for visually demanding tasks. Markov‐based measures of dynamic eye movements provided insights to better understand drivers’ underlying mental processes and scanning strategies, compared with typical static measures. The presented methods and results can be useful for in‐vehicle systems design and for further analysis of visual scanning patterns in the transportation domain.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/1/hfm20798_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151975/2/hfm20798.pd

Implementation of connected and autonomous vehicles in cities could have neutral effects on the total travel time costs: modeling and analysis for a circular city

Autonomous vehicles promise to revolutionize the automobile market, although their implementation could take several decades in which both types of cars will coexist on the streets. We formulate a model for a circular city based on continuous approximations, considering demand surfaces over the city. Numerical results from our model predict direct and indirect effects of connected and autonomous vehicles. Direct effects will be positive for our cities: (a) less street supply is needed to accommodate the traffic; (b) congestion levels decrease: travel costs may decrease by 30%. Some indirect effects will counterbalance these positive effects: (c) a decrease of 20% in the value of travel time can reduce the total cost by a third; (d) induced demand could be as high as 50%, bringing equivalent total costs in the future scenario; (e) the vehicle-kilometers traveled could also affect the future scenario; and (f) increases in city size and urban sprawl. As a conclusion, the implementation of autonomous vehicles could be neutral for the cities regarding travel time costs. City planning agencies still have to promote complementary modes such as active mobility (walking and bicycle), transit (public transportation), and shared mobility (shared autonomous vehicles and mobility as a service).Peer ReviewedPostprint (published version

aiMotive Dataset: A Multimodal Dataset for Robust Autonomous Driving with Long-Range Perception

Autonomous driving is a popular research area within the computer vision research community. Since autonomous vehicles are highly safety-critical, ensuring robustness is essential for real-world deployment. While several public multimodal datasets are accessible, they mainly comprise two sensor modalities (camera, LiDAR) which are not well suited for adverse weather. In addition, they lack far-range annotations, making it harder to train neural networks that are the base of a highway assistant function of an autonomous vehicle. Therefore, we introduce a multimodal dataset for robust autonomous driving with long-range perception. The dataset consists of 176 scenes with synchronized and calibrated LiDAR, camera, and radar sensors covering a 360-degree field of view. The collected data was captured in highway, urban, and suburban areas during daytime, night, and rain and is annotated with 3D bounding boxes with consistent identifiers across frames. Furthermore, we trained unimodal and multimodal baseline models for 3D object detection. Data are available at \url{https://github.com/aimotive/aimotive_dataset}.Comment: The paper was accepted to ICLR 2023 Workshop Scene Representations for Autonomous Drivin

Profile-speed data-based models to estimate operating speeds for urban residential streets with a 30km/h speed limit

AbstractA speed limit of 30kilometres per hour (km/h) has been widely introduced for urban residential streets to ensure traffic safety and allow these streets to fulfil other intended functions. However, excessive speeds on these roads are very common, causing traffic safety problems and threatening the liveability of neighbourhoods. An effective and active way to deal with speeding is the application of a performance-based design approach, as mentioned in previous research. In a performance-based design approach, street geometrics and roadside elements are selected based on their influence on the desired driving speeds. The relationship between driving speeds and street features therefore needs to be determined. Although several studies have developed operating speed models for urban streets, all of these models were calibrated based on data for streets with speed limits of more than 30km/h. The present research is designed to investigate the influence of various roadway and roadside characteristics on operating speeds on urban tangent street sections with a 30km/h speed limit using profile-speed data. A simultaneous equation regression with a three-stage-least-square (3SLS) estimator was used for the modelling effort. The driving speed models developed in this study incorporate several street design factors, which provide helpful information for urban planners and street designers to cope with speeding issues on residential streets

Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting

Forecasting how landslides will evolve over time or whether they will fail is a challenging task due to a variety of factors, both internal and external. Despite their considerable potential to address these challenges, deep learning techniques lack interpretability, undermining the credibility of the forecasts they produce. The recent development of transformer-based deep learning offers untapped possibilities for forecasting landslides with unprecedented interpretability and nonlinear feature learning capabilities. Here, we present a deep learning pipeline that is capable of predicting landslide behavior holistically, which employs a transformer-based network called LFIT to learn complex nonlinear relationships from prior knowledge and multiple source data, identifying the most relevant variables, and demonstrating a comprehensive understanding of landslide evolution and temporal patterns. By integrating prior knowledge, we provide improvement in holistic landslide forecasting, enabling us to capture diverse responses to various influencing factors in different local landslide areas. Using deformation observations as proxies for measuring the kinetics of landslides, we validate our approach by training models to forecast reservoir landslides in the Three Gorges Reservoir and creeping landslides on the Tibetan Plateau. When prior knowledge is incorporated, we show that interpretable landslide forecasting effectively identifies influential factors across various landslides. It further elucidates how local areas respond to these factors, making landslide behavior and trends more interpretable and predictable. The findings from this study will contribute to understanding landslide behavior in a new way and make the proposed approach applicable to other complex disasters influenced by internal and external factors in the future