Browsing by Author "Kamalanathsharma, Raj Kishore"

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    AERIS : Eco-Vehicle Speed Control at Signalized Intersections Using I2V Communication
    Rakha, Hesham A.; Kamalanathsharma, Raj Kishore; Ahn, Kyoungho (United States. Joint Program Office for Intelligent Transportation Systems, 2012-06)
    This report concentrates on a velocity advisory tool, or decision support system, for vehicles approaching an intersection using communication capabilities between the infrastructure and vehicles. The system uses available signal change information, vehicle characteristics, lead vehicle characteristics, and intersection features to compute the fuel-optimal vehicle trajectory. The proposed system involves a complex optimization logic incorporating roadway characteristics, lead vehicle information, vehicle acceleration capabilities and microscopic fuel consumption models to generate a fuel-optimal speed profile. The research also develops a MATLAB application in order to demonstrate the potential of an in-vehicle application of such a technology.
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    Agent-Based Game Theory Modeling for Driverless Vehicles at Intersections
    Rakha, Hesham A.; Zohdy, Ismail H.; Kamalanathsharma, Raj Kishore (United States. Department of Transportation, 2013-02-19)
    This report presents three research efforts that were published in various journals. The first research effort presents a reactive-driving agent based algorithm for modeling driver left turn gap acceptance behavior at signalized intersections. This model considers the interaction between driver characteristics and vehicle physical capabilities. The model explicitly captures the vehicle constraints on driving behavior using a vehicle dynamics model. In addition, the model uses the driver's input and the psychological deliberation in accepting/rejecting a gap. The model is developed using a total of 301 accepted gaps and subsequently validated using 2,429 rejected gaps at the same site and also validated using 1,485 gap decisions (323 accepted and 1,162 rejected) at another site. The proposed model is considered as a mix between traditional and reactive methods for decision making and consists of three main components: input, data processing and output. The input component uses sensing information, vehicle and driver characteristics to process the data and estimate the critical gap value. Thereafter, the agent decides to either accept or reject the offered gap by comparing to a driver-specific critical gap (the offered gap should be greater than the critical gap for it to be accepted). The results demonstrate that the agent-based model is superior to the standard logistic regression model because it produces consistent performance for accepted and rejected gaps (correct predictions in 90% of the observations) and the model is easily transferable to different sites. The proposed modeling framework can be generalized to capture different vehicle types, roadway configurations, traffic movements, intersection characteristics, and weather effects on driver gap acceptance behavior. The findings of this research effort is considered as an essential stage for modeling autonomous/driverless vehicles The second effort develops a heuristic optimization algorithm for automated vehicles (equipped with cooperative adaptive cruise control CACC systems) at uncontrolled intersections using a game theory framework. The proposed system models the automated vehicles as reactive agents interacting and collaborating with the intersection controller (manager agent) to minimize the total delay. The system is evaluated using a case study considering two different intersection control scenarios: a four-way stop control and the proposed intersection controller framework. In both scenarios, four automated vehicles (a single vehicle per approach) were simulated using a Monte Carlo simulation that was repeated 1000 times. The results show that the proposed system reduces the total delay relative to a traditional stop control by 35 seconds on average, which corresponds to an approximately 70 percent reduction in the total delay. The third effort presents a new tool for optimizing the movements of autonomous/driverless vehicles through intersections: iCACC. The main concept of the proposed tool is to control vehicle trajectories using Cooperative Adaptive Cruise Control (CACC) systems to avoid collisions and minimize intersection delay. Simulations were executed to compare conventional signal control with iCACC considering two measures of effectiveness - delay and fuel consumption. Savings in delay and fuel consumption in the range of 91 and 82 percent relative to conventional signal control were demonstrated, respectively. It is anticipated that the findings of this report may contribute in the future of advanced vehicles control and connected vehicles applications.
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    Eco-Driving in the Vicinity of Roadway Intersections - Algorithmic Development, Modeling and Testing
    Kamalanathsharma, Raj Kishore (Virginia Tech, 2014-05-06)
    Vehicle stops and speed variations account for a large percentage of vehicle fuel losses especially at signalized intersections. Recently, researchers have attempted to develop tools that reduce these losses by capitalizing on traffic signal information received via vehicle connectivity with traffic signal controllers. Existing state-of-the-art approaches, however, only consider surrogate measures (e.g. number of vehicle stops, time spent accelerating and decelerating, and/or acceleration or deceleration levels) in the objective function and fail to explicitly optimize vehicle fuel consumption levels. Furthermore, the majority of these models do not capture vehicle acceleration and deceleration limitations in addition to vehicle-to-vehicle interactions as constraints within the mathematical program. The connectivity between vehicles and infrastructure, as achieved through Connected Vehicles technology, can provide a vehicle with information that was not possible before. For example, information on traffic signal changes, traffic slow-downs and even headway and speed of lead vehicles can be shared. The research proposed in this dissertation uses this information and advanced computational models to develop fuel-efficient vehicle trajectories, which can either be used as guidance for drivers or can be attached to an electronic throttle controlled cruise control system. This fuel-efficient cruise control system is known as an Eco-Cooperative Adaptive Cruise Control (ECACC) system. In addition to the ECACC presented here, the research also expands on some of the key eco-driving concepts such as fuel-optimizing acceleration models, which could be used in conjunction with conventional vehicles and even autonomous vehicles, or assistive systems that are being implemented in vehicles. The dissertation first presents the results from an on-line survey soliciting driver input on public perceptions of in-vehicle assistive devices. The results of the survey indicate that user-acceptance to systems that enhance safety and efficiency is ranked high. Driver–willingness to use advanced in-vehicle technology and cellphone applications is also found to be subjective on what benefits it has to offer and safety and efficiency are found to be in the top list. The dissertation then presents the algorithmic development of an Eco-Cooperative Adaptive Cruise Control system. The modeling of the system constitutes a modified state-of-the-art path-finding algorithm within a dynamic programming framework to find near-optimal and near-real-time solutions to a complex non-linear programming problem that involves minimizing vehicle fuel consumption in the vicinity of signalized intersections. The results demonstrated savings of up to 30 percent in fuel consumption within the traffic signalized intersection vicinity. The proposed system was tested in an agent-based environment developed in MATLAB using the RPA car-following model as well as the Society of Automobile Engineers (SAE) J2735 message set standards for vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. The results showed how multi-vehicle interaction enhances usability of the system. Simulation of a calibrated real intersection showed average fuel savings of nearly 30 percent for peak volumes. The fuel reduction was high for low volumes and decreased as the traffic volumes increased. The final testing of the algorithm was done in an enhanced Traffic Experimental Simulation tool (eTEXAS) that incorporates the conventional TEXAS model with a new web-service interface as well as connected vehicle message set dictionary. This testing was able to demonstrate model corrections required to negate the effect of system latencies as well as a demonstration of using SAE message set parsing in a connected vehicle application. Finally, the dissertation develops an integrated framework for the control of autonomous vehicle movements through intersections using a multi-objective optimization algorithm. The algorithm integrated within an existing framework that minimizes vehicle delay while ensuring vehicles do not collide. A lower-level of control is introduced that minimizes vehicle fuel consumption subject to the arrival times assigned by the upper-level controller. Results show that the eco-speed control algorithm was able to reduce the overall fuel-consumption of autonomous vehicles passing through an intersection by 15 percent while maintaining the 80 percent saving in delay when compared to a traditional signalized intersection control.
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    Field Testing of Eco-Speed Control Using V2I Communication
    Rakha, Hesham A.; Chen, Hao; Almannaa, Mohammed Hamad; Kamalanathsharma, Raj Kishore; El-Shawarby, Ihab; Loulizi, Amara (Connected Vehicle/Infrastructure University Transportation Center (CVI-UTC), 2016-04-15)
    This research focused on the development of an Eco-Cooperative Adaptive Cruise Control (Eco-CACC) System and addressed the implementation issues associated with applying it in the field. The Eco-CACC system computes and recommends a fuel-efficient speed based on Signal Phasing and Timing (SPaT) data received from the traffic signal controller via vehicle-to-infrastructure (V2I) communication. The computed speed profile can either be broadcast as an audio alert to the driver to manually control the vehicle, or, implemented in an automated vehicle (AV) to automatically control the vehicle. The proposed system addresses all possible scenarios, algorithmically, that a driver may encounter when approaching a signalized intersection. Additionally, from an implementation standpoint, the research addresses the challenges associated with communication latency, data errors, real-time computation, and ride smoothness. The system was tested on the Virginia Smart Road Connected Vehicle Test Bed in Blacksburg, VA. Four scenarios were tested for each participant: a base driving scenario, where no speed profile data was communicated; a scenario in which the driver was provided with a “time to red light” countdown; a manual Eco-CACC scenario where the driver was instructed to follow a recommended speed profile given via audio alert; and finally, an automated Eco-CACC scenario where the AV system controlled the vehicle’s longitudinal motion. The field test included 32 participants, and each participant completed 64 trips to pass through a signalized intersection for different combinations of signal timing and road grades. The analyzed results demonstrate the benefits of the Eco-CACC system in assisting vehicles to drive smoothly in the vicinity of intersections, thereby reducing fuel consumption levels and travel times. Compared to an uninformed baseline drive, the longitudinally automated Eco-CACC system controlled vehicle drive resulted in savings in fuel consumption levels and travel times of approximately 37.8% and 9.3%, respectively.
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    Survey on In-vehicle Technology Use: Results and Findings
    Kamalanathsharma, Raj Kishore; Rakha, Hesham A.; Zohdy, Ismail H. (Elsevier, 2015)
    The use of advanced technology in automobiles has increased dramatically in the past couple of years. Driver-assisting gadgets such as navigation systems, advanced cruise control, collision avoidance systems, and other safety systems have moved down the ladder from luxury to more basic vehicles. Concurrently, auto manufacturers are also designing and testing driving algorithms that can assist with basic driving tasks, many of which are being continuously scrutinized by traffic safety agencies to ensure that these systems do not pose a safety hazard. The research presented in this paper brings a third perspective to in-vehicle technology by conducting a two-stage survey to collect public opinion on advanced in-vehicle technology. Approximately 64 percent of the respondents used a smartphone application to assist with their travel. The top-used applications were navigation and real-time traffic information systems. Among those who used smartphones during their commutes, the top-used applications were navigation and entertainment.
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    Traffic Adaptive Offset-Based Preemption for Emergency Vehicles
    Kamalanathsharma, Raj Kishore (Virginia Tech, 2010-08-10)
    This research analyzed and evaluated a new strategy for preemption of emergency vehicles along a corridor, which is route-based and adaptive to real-time traffic conditions. The method uses dynamic offsets which are adjusted using congestion levels to provide uninterrupted preempted green signal for the emergency vehicle throughout its route. By achieving a higher average emergency vehicle speed, this method promises faster emergency response which results in saving life and property as well as larger emergency service radius for the dispatch stations. The research evaluated the effectiveness of two possible algorithms for offset adjustment using measured vehicle queues. It is showed to reduce the emergency vehicle travel-time by 31 percent when compared to cases without preemption and 13 percent when compared to traditional method of individual-intersection preemption.