Road injuries are rated among the top 10 causes of death
by the World Health Organization, and the only one that is not a
disease. The total economic cost of motor vehicle crashes in the
United States was estimated to be $242 billion a year. This study
examines multiple factors of accidents simultaneously with a goal of
generating an interpretable model that can predict the occurrence of
an accident given road conditions and driver behavior. The study
compared 4 machine learning and deep learning modeling
techniques on a dataset of 7707 trips collected by the Second
Strategic Highway Research Program. A gradient boosted model was
found to be most accurate and interpretable in accident prediction.
This modeling technique also allows us to rank the feature
importance of the factors in the model. The study finds that driver
behavior, pre-incident maneuvers and secondary task duration are
the most important variables in the predictive model. Using these
conclusions will allow us to perform more work to infer these
accident causes directly from vehicle sensor data in the future.
more »
« less
This content will become publicly available on February 26, 2025
Dilemma Zone: A Comprehensive Study of Influential Factors and Behavior Analysis
In this paper, we examine the problem of the
Dilemma Zone (DZ) in depth, weaving together the various
influences that span the environment, the ego-vehicle, and
ultimately the characteristics of the driver. Driver behavior
in dilemma zone situations is crucial, and more research is
urgently needed in this area. The journey through various
modeling approaches and data acquisition techniques sheds new
light on driver behavior within the dilemma zone context. Our
thorough examination of the current research landscape has
revealed that several significant areas remain overlooked. As
well as the dynamic impact of vehicles, vehicle interactions,
and a strong tendency to over-rely on infrastructure information,
there are also concerns about the lack of comprehensive
evaluation tools. However, we do not see these gaps
as stumbling blocks, but rather as steppingstones for future
research opportunities. A more focused study of cooperative
solutions is required considering the potential of personalized
modeling, the untapped power of machine learning techniques,
and the importance of personalized modeling. It is our hope
that by embracing innovative approaches that can capture and
simulate personalized behavioral data using “everything-in-the-loop”
simulations, future research endeavors will be guided.
To effectively mitigate the DZ problem, we also point out the
research gaps and opportunities for further research in the DZ.
more »
« less
- Award ID(s):
- 2152258
- NSF-PAR ID:
- 10510372
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-7066-9
- Page Range / eLocation ID:
- 1 to 8
- Format(s):
- Medium: X
- Location:
- Riverside, CA, USA
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Objective This study investigated the use of human performance modeling (HPM) approach for prediction of driver behavior and interactions with in-vehicle technology.
Background HPM has been applied in numerous human factors domains such as surface transportation as it can quantify and predict human performance; however, there has been no integrated literature review for predicting driver behavior and interactions with in-vehicle technology in terms of the characteristics of methods used and variables explored.
Method A systematic literature review was conducted using Compendex, Web of Science, and Google Scholar. As a result, 100 studies met the inclusion criteria and were reviewed by the authors. Model characteristics and variables were summarized to identify the research gaps and to provide a lookup table to select an appropriate method.
Results The findings provided information on how to select an appropriate HPM based on a combination of independent and dependent variables. The review also summarized the characteristics, limitations, applications, modeling tools, and theoretical bases of the major HPMs.
Conclusion The study provided a summary of state-of-the-art on the use of HPM to model driver behavior and use of in-vehicle technology. We provided a table that can assist researchers to find an appropriate modeling approach based on the study independent and dependent variables.
Application The findings of this study can facilitate the use of HPM in surface transportation and reduce the learning time for researchers especially those with limited modeling background.
-
Connected and automated vehicles (CAVs) are sup- posed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic envi- ronment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to under- stand HDV behaviors to make safe actions. In this study, we develop a driver digital twin (DDT) for the online prediction of personalized lane-change behavior, allowing CAVs to predict surrounding vehicles’ behaviors with the help of the digital twin technology. DDT is deployed on a vehicle-edge–cloud architec- ture, where the cloud server models the driver behavior for each HDV based on the historical naturalistic driving data, while the edge server processes the real-time data from each driver with his/her digital twin on the cloud to predict the personalized lane- change maneuver. The proposed system is first evaluated on a human-in-the-loop co-simulation platform, and then in a field implementation with three passenger vehicles driving along an on/off ramp segment connecting to the edge server and cloud through the 4G/LTE cellular network. The lane-change intention can be recognized in 6 s on average before the vehicle crosses the lane separation line, and the Mean Euclidean Distance between the predicted trajectory and GPS ground truth is 1.03 m within a 4-s prediction window. Compared to the general model, using a personalized model can improve prediction accuracy by 27.8%. The demonstration video of the proposed system can be watched at https://youtu.be/5cbsabgIOdM.more » « less
-
more » « less
Abstract Conflict between livestock producers and wild predators is a central driver of large predator declines and simultaneously may imperil the lives and livelihoods of livestock producers. There is a growing recognition that livestock–predator conflict is a socio‐ecological problem, but few case studies exist to guide conflict research and management from this point of view. Here we present a case study of coyote‐sheep predation on a California ranch in which we combine methods from the rapidly growing field of predation risk modeling with participatory mapping of perceptions of predation risk. Our findings reveal an important selection bias that may occur when producer perceptions and decisions are excluded from ecological methods of studying conflict. We further demonstrate how producer inputs, participatory mapping, and ecological modeling of conflict can inform one another in understanding patterns, drivers, and management opportunities for livestock–predator conflict. Finally, we make recommendations for improving the interoperability of ecological and social data about predation risk. Collectively our methods offer a socio‐ecological approach that fills important research gaps and offers guidance to future research.
-
Traditional ground vehicle architectures comprise of a chassis connected via passive, semi-active, or active suspension systems to multiple ground wheels. Current design-optimizations of vehicle architectures for on-road applications have diminished their mobility and maneuverability in off-road settings. Autonomous Ground Vehicles (AGV) traversing off-road environments face numerous challenges concerning terrain roughness, soil hardness, uneven obstacle-filled terrain, and varying traction conditions. Numerous Active Articulated-Wheeled (AAW) vehicle architectures have emerged to permit AGVs to adapt to variable terrain conditions in various off-road application arenas (off-road, construction, mining, and space robotics). However, a comprehensive framework of AAW platforms for exploring various facets of system architecture/design, analysis (kinematics/dynamics), and control (motions/forces) remains challenging. While current literature on the AAW system incorporates modeling and control from the legged and wheeled-legged robots community, it lacks a systematic process of architecture selection and motion control that should be developed around critical quantifiable performance parameters. This paper will: (i) analyze a broad body of literature; and (ii) identify modeling and control techniques that can enable the efficient development of AAW platforms. We then analyze key performance measures with respect to traversability, maneuverability, and terrainability, along with an experimental simulation of an AAW vehicle traversing over uneven terrain and how active articulation could achieve some of the critical performance measures. Against the performance parameters, gaps within the existing literature and opportunities for further research are identified to potentially enhance AAW platforms’ performance.more » « less
