scholarly journals Real-Time Driving Behavior Identification Based on Multi-Source Data Fusion

2021 ◽  
Vol 19 (1) ◽  
pp. 348
Author(s):  
Yongfeng Ma ◽  
Zhuopeng Xie ◽  
Shuyan Chen ◽  
Ying Wu ◽  
Fengxiang Qiao
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Time Window ◽  
Time Windows ◽  
Driving Behavior ◽  
Optimal Time ◽  
Lane Changing ◽  
Kinematic Data ◽  
Wide Range ◽  
Driving Assistance

Real-time driving behavior identification has a wide range of applications in monitoring driver states and predicting driving risks. In contrast to the traditional approaches that were mostly based on a single data source with poor identification capabilities, this paper innovatively integrates driver expression into driving behavior identification. First, 12-day online car-hailing driving data were collected in a non-intrusive manner. Then, with vehicle kinematic data and driver expression data as inputs, a stacked Long Short-Term Memory (S-LSTM) network was constructed to identify five kinds of driving behaviors, namely, lane keeping, acceleration, deceleration, turning, and lane changing. The Artificial Neural Network (ANN) and XGBoost algorithms were also employed as a comparison. Additionally, ten sliding time windows of different lengths were introduced to generate driving behavior identification samples. The results show that, using all sources of data yields better results than using the kinematic data only, with the average F1 value improved by 0.041, while the S-LSTM algorithm is better than the ANN and XGBoost algorithms. Furthermore, the optimal time window length is 3.5 s, with an average F1 of 0.877. This study provides an effective method for real-time driving behavior identification, and thereby supports the driving pattern analysis and Advanced Driving Assistance System.

scholarly journals An Optimization Method of Time Window Based on Travel Time and Reliability

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Fengjie Fu ◽  
Dongfang Ma ◽  
Dianhai Wang ◽  
Wei Qian
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Time Window ◽  
Time Windows ◽  
Time Estimation ◽  
Optimization Method ◽  
Time Change ◽  
Basic Unit ◽  
Optimal Time ◽  
Travel Time Estimation

The dynamic change of urban road travel time was analyzed using video image detector data, and it showed cyclic variation, so the signal cycle length at the upstream intersection was conducted as the basic unit of time window; there was some evidence of bimodality in the actual travel time distributions; therefore, the fitting parameters of the travel time bimodal distribution were estimated using the EM algorithm. Then the weighted average value of the two means was indicated as the travel time estimation value, and the Modified Buffer Time Index (MBIT) was expressed as travel time variability; based on the characteristics of travel time change and MBIT along with different time windows, the time window was optimized dynamically for minimum MBIT, requiring that the travel time change be lower than the threshold value and traffic incidents can be detected real time; finally, travel times on Shandong Road in Qingdao were estimated every 10 s, 120 s, optimal time windows, and 480 s and the comparisons demonstrated that travel time estimation in optimal time windows can exactly and steadily reflect the real-time traffic. It verifies the effectiveness of the optimization method.

The Robust Vehicle Routing Problem with Time Window Assignments

2021 ◽  
Vol 55 (2) ◽  
pp. 395-413
Author(s):  
Maaike Hoogeboom ◽  
Yossiri Adulyasak ◽  
Wout Dullaert ◽  
Patrick Jaillet
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Service Providers ◽  
Time Window ◽  
Time Windows ◽  
Branch And Cut ◽  
Optimal Time ◽  
Routing Problem ◽  
Solution Approach

In practice, there are several applications in which logistics service providers determine the service time windows at the customers, for example, in parcel delivery, retail, and repair services. These companies face uncertain travel times and service times that have to be taken into account when determining the time windows and routes prior to departure. The objective of the proposed robust vehicle routing problem with time window assignments (RVRP-TWA) is to simultaneously determine routes and time window assignments such that the expected travel time and the risk of violating the time windows are minimized. We assume that the travel time probability distributions are not completely known but that some statistics, such as the mean, minimum, and maximum, can be estimated. We extend the robust framework based on the requirements’ violation index, which was originally developed for the case where the specific requirements (time windows) are given as inputs, to the case where they are also part of the decisions. The subproblem of finding the optimal time window assignment for the customers in a given route is shown to be convex, and the subgradients can be derived. The RVRP-TWA is solved by iteratively generating subgradient cuts from the subproblem that are added in a branch-and-cut fashion. Experiments address the performance of the proposed solution approach and examine the trade-off between expected travel time and risk of violating the time windows.

scholarly journals Prediction of Lane-Changing Maneuvers with Automatic Labeling and Deep Learning

2020 ◽  
Vol 2674 (7) ◽  
pp. 336-347 ◽  
Author(s):  
Vishal Mahajan ◽  
Christos Katrakazas ◽  
Constantinos Antoniou
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Highway Safety ◽  
Unlabeled Data ◽  
Support Vector ◽  
False Alarms ◽  
Lane Changing ◽  
Lane Changes ◽  
Significant Research

Highway safety has attracted significant research interest in recent years, especially as innovative technologies such as connected and autonomous vehicles (CAVs) are fast becoming a reality. Identification and prediction of driving intention are fundamental for avoiding collisions as it can provide useful information to drivers and vehicles in their vicinity. However, the state-of-the-art in maneuver prediction requires the utilization of large labeled datasets, which demand a significant amount of processing and might hinder real-time applications. In this paper, an end-to-end machine learning model for predicting lane-change maneuvers from unlabeled data using a limited number of features is developed and presented. The model is built on a novel comprehensive dataset (i.e., highD) obtained from German highways with camera-equipped drones. Density-based clustering is used to identify lane-changing and lane-keeping maneuvers and a support vector machine (SVM) model is then trained to learn the boundaries of the clustered labels and automatically label the new raw data. The labeled data are then input to a long short-term memory (LSTM) model which is used to predict maneuver class. The classification results show that lane changes can efficiently be predicted in real-time, with an average detection time of at least 3 s with a small percentage of false alarms. The utilization of unlabeled data and vehicle characteristics as features increases the prospects of transferability of the approach and its practical application for highway safety.

scholarly journals Time Window-of-Opportunity Strategies for Oil Spill Planning and Response

1999 ◽  
Vol 71 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Atle B. Nordvik
Keyword(s):  
Decision Making ◽  
Oil Spill ◽  
Time Window ◽  
Time Windows ◽  
Cost Effective ◽  
Field Studies ◽  
Insurance Companies ◽  
Specific Response ◽  
Wide Range ◽  
Oil Spill Response

This paper presents an integrated scientific and engineering strategy to improve and bring planning and decision-making for marine oil spill response to a higher level of knowledge. The most efficient, environmentally preferred, and cost effective spill response is dependent on the following factors: chemistry of the spilled product, quantity, location, response time, environmental conditions, and effectiveness of available response technologies at various degrees of oil weathering.Time windows is a highly targeted process, in which the selection of response technologies will be more efficient, cost effective, technically correct, and environmentally sensitive and appropriate. The strategy integrates dynamic oil weathering data and performance effectiveness data for oil spill response technologies derived from laboratory, mesoscale, and experimental field studies. Performance data has been developed from a wide range of viscosities of different weathering stages of transported oils into a dynamic oil weathering database to identify and estimate time periods, called "technology windows-of-opportunity." In these windows, specific response methods, technologies, equipment, or products are more effective during clean-up operations for specific oils. The data bases represent the state of the art for response technologies and research in oil spill response.The strategy provides a standard foundation for rapid and cost effective oil spill response decision-making, and is intended for use by local, state, federal agencies, response planners, clean up organizations (responders), insurance companies, tanker owners, and transporters. It provides policy, planners and decision-makers with a scientifically based and documented "tool" in oil spill response that has not been available before.

Therapeutic hypothermia in acute ischemic stroke

2011 ◽  
Vol 30 (6) ◽  
pp. E17 ◽  
Author(s):  
Leonid I. Groysman ◽  
Benjamin A. Emanuel ◽  
May A. Kim-Tenser ◽  
Gene Y. Sung ◽  
William J. Mack
Keyword(s):  
Acute Stroke ◽  
Therapeutic Hypothermia ◽  
Time Window ◽  
Time Windows ◽  
Optimal Time ◽  
Ischemic Cerebrovascular Disease ◽  
The Past ◽  
Clinical Indications ◽  
Temperature Regimen ◽  
Translational Studies

Induced hypothermia has been used for neuroprotection in cardiac and neurovascular procedures. Experimental and translational studies provide evidence for its utility in the treatment of ischemic cerebrovascular disease. Over the past decade, these principles have been applied to the clinical management of acute stroke. Varying induction methods, time windows, clinical indications, and adjuvant therapies have been studied. In this article the authors review the mechanisms and techniques for achieving therapeutic hypothermia in the setting of acute stroke, and they outline pertinent side effects and complications. The manuscript summarizes and examines the relevant clinical trials to date. Despite a reasonable amount of existing data, this review suggests that additional trials are warranted to define the optimal time window, temperature regimen, and precise clinical indications for induction of therapeutic hypothermia in the setting of acute stroke.

scholarly journals State Machine Approach for Lane Changing Driving Behavior Recognition

Automation ◽  
2020 ◽  
Vol 1 (1) ◽  
pp. 68-79
Author(s):  
Ruth David ◽  
Sandra Rothe ◽  
Dirk Söffker
Keyword(s):  
Driving Simulator ◽  
Driving Behavior ◽  
State Machine ◽  
The State ◽  
Behavior Recognition ◽  
Lane Changing ◽  
Recognition Model ◽  
Driving Behaviors ◽  
Current State ◽  
Driving Assistance

Research in understanding human behavior is a growing field within the development of Advanced Driving Assistance Systems (ADASs). In this contribution, a state machine approach is proposed to develop a driving behavior recognition model. The state machine approach is a behavior model based on the current state and a given set of inputs. Transitions to different states occur or we remain in the same state producing outputs. The transition between states depends on a set of environmental and driving variables. Based on a heuristic understanding of driving situations modeled as states, as well as one of the related actions modeling the state, using an assumed relation between them as the state machine topology, in this paper, a crisp approach is applied to adapt the model to real behaviors. An important aspect of the contribution is to introduce a trainable state machine-based model to describe drivers’ lane changing behavior. Three driving maneuvers are defined as states. The training of the model is related to the definition/tuning of transition variables (and state definitions). Here, driving data are used as the input for training. The non-dominated sorting genetic algorithm II is used to generate the optimized transition threshold. Comparing the data of actual human driving behaviors collected using driving simulator experiments and the calculated driving behaviors, this approach is able to develop a personalized behavior recognition model. The newly established algorithm presents an easy to apply, reliable, and interpretable AI approach.

Time Window from Visual Images to Visual Short-Term Memory: Consolidation or Integration?

2004 ◽  
Vol 51 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Yuhong Jiang
Keyword(s):  
Short Term Memory ◽  
Time Window ◽  
Short Interval ◽  
Visual Images ◽  
Short Term ◽  
Temporal Window ◽  
Term Memory ◽  
Visual Short Term Memory ◽  
Probe Task ◽  
Long Time

Abstract. When two dot arrays are briefly presented, separated by a short interval of time, visual short-term memory of the first array is disrupted if the interval between arrays is shorter than 1300-1500 ms ( Brockmole, Wang, & Irwin, 2002 ). Here we investigated whether such a time window was triggered by the necessity to integrate arrays. Using a probe task we removed the need for integration but retained the requirement to represent the images. We found that a long time window was needed for performance to reach asymptote even when integration across images was not required. Furthermore, such window was lengthened if subjects had to remember the locations of the second array, but not if they only conducted a visual search among it. We suggest that a temporal window is required for consolidation of the first array, which is vulnerable to disruption by subsequent images that also need to be memorized.

scholarly journals Global soil moisture data derived through machine learning trained with in-situ measurements

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Sungmin O. ◽  
Rene Orth
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Large Scale ◽  
Short Term Memory ◽  
Temporal Dynamics ◽  
Soil Moisture Data ◽  
Wide Range ◽  
Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

Lateral obstacle avoidance control based on driving behavior recognition of the preceding vehicles in adjacent lanes

2020 ◽  
Vol 68 (10) ◽  
pp. 880-892
Author(s):  
Youguo He ◽  
Xing Gong ◽  
Chaochun Yuan ◽  
Jie Shen ◽  
Yingkui Du
Keyword(s):  
Obstacle Avoidance ◽  
Lateral Displacement ◽  
Longitudinal Velocity ◽  
Driving Behavior ◽  
Lateral Acceleration ◽  
Lateral Motion ◽  
Behavior Recognition ◽  
Dynamic Surface ◽  
Lane Changing ◽  
The Impact

AbstractThis paper proposes a lateral lane change obstacle avoidance constraint control simulation algorithm based on the driving behavior recognition of the preceding vehicles in adjacent lanes. Firstly, the driving behavior of the preceding vehicles is recognized based on the Hidden Markov Model, this research uses longitudinal velocity, lateral displacement and lateral velocity as the optimal observation signals to recognize the driving behaviors including lane-keeping, left-lane-changing or right-lane-changing; Secondly, through the simulation of the dangerous cutting-in behavior of the preceding vehicles in adjacent lanes, this paper calculates the ideal front wheel steering angle according to the designed lateral acceleration in the process of obstacle avoidance, designs the vehicle lateral motion controller by combining the backstepping and Dynamic Surface Control, and the safety boundary of the lateral motion is constrained based on the Barrier Lyapunov Function; Finally, simulation model is built, and the simulation results show that the designed controller has good performance. This active safety technology effectively reduces the impact on the autonomous vehicle safety when the preceding vehicle suddenly cuts into the lane.

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