Study of Intelligent Human Machine Interface based on Driving Simulator

poster abstractIn this project, we examine how driving task performance metrics are affected when drivers have to complete certain typical tasks associated with the in-vehicle infotainment system and peripheral devices. Detailed experiment procedures are designed and data are collected through a driving simulator. The collected data are analyzed to study how the task completion time and quality of driving are affected by the control that is required to complete the in-vehicle secondary tasks

Analysis of Potential Co-Benefits for Bicyclist Crash Imminent Braking Systems

In the US, the number of traffic fatalities has had a long term downward trend as a result of advances in the crash worthiness of vehicles. However, these improvements in crash worthiness do little to protect other vulnerable road users such as pedestrians or bicyclists. Several manufacturers have developed a new generation of crash avoidance systems that attempt to recognize and mitigate imminent crashes with non-motorists. While the focus of these systems has been on pedestrians where they can make meaningful contributions to improved safety [1], recent designs of these systems have recognized mitigating bicyclist crashes as a potential co-benefit. This paper evaluates the performance of one system that is currently available for consumer purchase. Because the vehicle manufacturer does not claim effectiveness for their system under all crash geometries, we focus our attention on the crash scenario that has the highest social cost in the US: the cyclist and vehicle on parallel paths being struck from behind. Our analysis of co benefits examines the ability to reduce three measures: number of crashes, fatalities, and a comprehensive measure for social cost that incorporates morbidity and mortality. Test track simulations under realistic circumstances with a realistic surrogate bicyclist target are conducted. Empirical models are developed for system performance and potential benefits for injury and fatality reduction. These models identify three key variables in the analysis: vehicle speed, cyclist speed and cyclist age as key determinants of potential co-benefits. We find that the evaluated system offers only limited benefits for any but the oldest bicycle riders for our tested scenario

Obtain a Simulation Model of a Pedestrian Collision Imminent Braking System Based on the Vehicle Testing Data

Forward pedestrian collision imminent braking (CIB) systems has proven to be of great significance in improving road safety and protecting pedestrians. Since pedestrian CIB technology is not mature, the performance of different pedestrian CIB systems varies significantly. Therefore the simulation of a CIB system needs to be vehicle specific. The CIB simulation can be based on the component sensor parameters and decision making rules. Since these parameters and decision rules for on the market vehicles are not available outside of vehicle manufactures, it is difficult for the general research communities to develop a good CIB simulation model based on this approach. To solve this problem, this study presents a new method for developing a pedestrian CIB simulation model using pedestrian CIB testing data. The implementation was in PreScan. The simulation results demonstrate that a pedestrian CIB simulation model developed using this methodology could reflect the behavior of a real vehicle equipped with pedestrian CIB system

Performance Measurement of Vehicle Crash Imminent Braking Systems

poster abstractAs active safety systems have been introduced to passenger vehicles, there is an immediate need to develop a standardized testing protocol and scoring mechanism which enables an objective comparison between the performance of active safety systems implemented across various vehicle platforms. This project proposes a methodology for the establishment of such standards to evaluate and compare the performance of Crash Imminent Braking (CIB) systems. The proposed scoring mechanism is implemented based on track testing data in the evaluation of a 2011 model year passenger vehicle equipped with a CIB system

Two-Stage Method for Optimal Operation of a Distributed Energy System

In this paper, a gas turbine-based distributed energy system (DES) model is developed for the design of operation planning. An operation mode aimed to optimize the operation of this DES is proposed. A multi-objective cost function considering the total system efficiency and operational cost is formulated for the optimal design of DES operation and control. A two-stage approach combining the particle swarm algorithm (PSO) with the sequential quadratic programming (SQP) method is employed to solve the nonlinear programming problem. Optimal operation strategies for the DES are investigated using the proposed two-stage method under three different demand loads in terms of weather conditions. The simulation results are compared with those using traditional rule-based operation methods. It is found that under the proposed operation mode, the DES is capable of achieving an improved performance in terms of thermal efficiency and operational cost

Pedestrian Detection based on Clustered Poselet Models and Hierarchical And-Or Grammar

In this paper, a novel part-based pedestrian detection algorithm is proposed for complex traffic surveillance environments. To capture posture and articulation variations of pedestrians, we define a hierarchical grammar model with the and-or graphical structure to represent the decomposition of pedestrians. Thus, pedestrian detection is converted to a parsing problem. Next, we propose clustered poselet models, which use the affinity propagation clustering algorithm to automatically select representative pedestrian part patterns in keypoint space. Trained clustered poselets are utilized as the terminal part models in the grammar model. Finally, after all clustered poselet activations in the input image are detected, one bottom-up inference is performed to effectively search maximum a posteriori (MAP) solutions in the grammar model. Thus, consistent poselet activations are combined into pedestrian hypotheses, and their bounding boxes are predicted. Both appearance scores and geometry constraints among pedestrian parts are considered in inference. A series of experiments is conducted on images, both from the public TUD-Pedestrian data set and collected in real traffic crossing scenarios. The experimental results demonstrate that our algorithm outperforms other successful approaches with high reliability and robustness in complex environments

Random Tur\'an and counting results for general position sets over finite fields

Let $\alpha(\mathbb{F}_q^d,p)$ denote the maximum size of a general position set in a $p$-random subset of $\mathbb{F}_q^d$. We determine the order of magnitude of $\alpha(\mathbb{F}_q^2,p)$ up to polylogarithmic factors for all possible values of $p$, improving the previous best upper bounds obtained by Roche-Newton--Warren and Bhowmick--Roche-Newton. For $d \ge 3$ we prove upper bounds for $\alpha(\mathbb{F}_q^d,p)$ that are essentially tight within certain intervals of $p$. We establish the upper bound $2^{(1+o(1))q}$ for the number of general position sets in $\mathbb{F}_q^d$, which matches the trivial lower bound $2^{q}$ asymptotically in exponent. We also refine this counting result by proving an asymptotically tight (in exponent) upper bound for the number of general position sets with fixed size. The latter result for $d=2$ improves a result of Roche-Newton--Warren. Our proofs are grounded in the hypergraph container method, and additionally, for $d=2$ we also leverage the pseudorandomness of the point-line incidence bipartite graph of $\mathbb{F}_{q}^2$.Comment: 24 pages(+2 pages for Appendix), 2 figure

Transportation Active Safety Institute

poster abstractSince its founding in February 2006, the mission of the Transportation Active Safety Institute (TASI) has been to advance the use of active safety systems to reduce vehicle crashes and save lives. TASI was one of 10 centers awarded IUPUI Signature Center funding (second round) in January, 2008. With core faculty drawn from ten departments representing eight schools at IUPUI, IUB and PUWL, the Transportation Active Safety Institute (TASI) is an interdisciplinary center for advanced transportation safety research and development on the IUPUI campus. Partnership with industry, government, and non-profit agencies ensures that university research activities complement existing technologies and address existing and future needs. TASI aims to provide a neutral forum for pre-competitive discussion and development of standards and test methodologies for establishing objective benefits of active-safety systems. TASI has established a driving simulator laboratory for research into driver behavior and for testing active safety system performance. The state-of-the-art DriveSafety DS-600c Driving Simulator is providing a flexible and realistic driving environment for industry, government, and internally sponsored research. This reconfigurable platform allows TASI to test various sensors and driver interfaces, in order to determine effective and convenient solutions to challenges in enhancing safety. Faculty members, research staff and graduate students have been working on several funded research projects such as human factors for semi-autonomous driving systems, intelligent human vehicle interfaces, real vehicle testing for crash-imminent braking system (autonomous braking system), distracted and impaired driving assessment, teen and older driver safety research, dealing with uncertainty in autonomous braking system, etc. TASI has also established an active dialog with other vehicle safety centers around the world through our “Global Academic Network for Active Safety.” Currently, global academic partners include Center for Automotive Research at the Ohio State University, National Advanced Driving Simulator at University of Iowa, University of Wisconsin, Tsinghua University in China, and Chalmers University of Technology in Sweden

Back of Queue Warning and Critical Information Delivery to Motorists

Back-of-queue crashes are one of the main sources for fatal accidents on U.S. highways. A variety of factors including low visibility, slippery road surface, and driver distraction/drowsiness during highway cruising, all contribute to this type of fatal crashes. Thus, it is very important to improve the driver’s situational awareness before they approach traffic queues on highways. In this project, we develop a prototype in-vehicle back-of-queue alerting system that is based on the probe vehicle data from INDOT. Speed changes among different road segments are used to identify slow traffic queues, which are compared with vehicle locations and moving directions to detect potential back-of-queue crashes. This prototype system is designed to issue alerting messages to drivers approaching the highway traffic queues via an Android-based smartphone app and an Android Auto device. The performance of this system has been evaluated using the driving simulator and a limited number of on-road test runs. The results showed the effectiveness and benefits of this prototype system

Development of Automated Incident Detection System Using Existing ATMS CCTV

Indiana Department of Transportation (INDOT) has over 300 digital cameras along highways in populated areas in Indiana. These cameras are used to monitor traffic conditions around the clock, all year round. Currently, the videos from these cameras are observed by human operators. The main objective of this research is to develop an automatic real-time system to monitor traffic conditions using the INDOT CCTV video feeds by a collaborative research team of the Transportation Active Safety Institute (TASI) at Indiana University-Purdue University Indianapolis (IUPUI) and the Traffic Management Center (TMC) of INDOT. In this project, the research team developed the system architecture based on a detailed system requirement analysis. The first prototype of major system components of the system has been implemented. Specifically, the team has successfully accomplished the following: An AI based deep learning algorithm provided in YOLO3 is selected for vehicle detection which generates the best results for daytime videos. The tracking information of moving vehicles is used to derive the locations of roads and lanes. A database is designed as the center place to gather and distribute the information generated from all camera videos. The database provides all information for the traffic incident detection. A web-based Graphical User Interface (GUI) was developed. The automatic traffic incident detection will be implemented after the traffic flow information being derived accurately. The research team is currently in the process of integrating the prototypes of all components of the system together to establish a complete system prototype