Ismail Zohdy

Intersection management for autonomous vehicles using iCACC

Recently several artificial intelligence labs have suggested the use of fully equipped vehicles with the capability of sensing the surrounding environment to enhance roadway safety. As a result, it is anticipated in the future that many vehicles will be autonomous and thus there is a need to optimize the movement of these vehicles. This paper presents a new tool for optimizing the movements of autonomous 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.

Intersection Management via Vehicle Connectivity: The Intersection Cooperative Adaptive Cruise Control System Concept

Since the introduction of the vehicle infrastructure integration (VII) and connected vehicle (CV) initiatives in the United States, numerous in-vehicle technologies based on wireless communications are currently being deployed. One of these technologies is cooperative adaptive cruise control (CACC) systems, which provide better connectivity, safety, and mobility by allowing vehicles to travel in denser platoons through vehicle-to-vehicle (V2V) communication. Accordingly, the research presented in this article develops a simulation/optimization tool that optimizes the movement of CACC-equipped vehicles as a replacement for traditional intersection control. This system, which is named iCACC, assumes that the intersection controller receives vehicle requests to travel through an intersection and advises each vehicle on the optimum course of action ensuring no crashes occur while at the same time minimizing the intersection delay. Four intersection control scenarios are compared, namely: a traffic signal, an all-way stop control (AWSC), a roundabout, and the iCACC controller. The results show that the proposed iCACC system significantly reduces the average intersection delay and fuel consumption level by 90 and 45%, respectively. Additionally, the article investigates the impact of vehicle dynamics, weather conditions, and level of market penetration of equipped vehicles on the future of automated vehicle control.

Game theory algorithm for intersection-based cooperative adaptive cruise control (CACC) systems

The paper 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) was 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.

Empirical Analysis of Effects of Wait Time and Rain Intensity on Driver Left-Turn Gap Acceptance Behavior

An empirical study was conducted to quantify the impact of several variables on driver left-turn gap acceptance behavior. These variables included gap duration, the drivers wait time in search of an appropriate acceptable gap, the time traveled by a driver to clear a conflict point, and the rain intensity. Gap acceptance data for a permissive left-turn maneuver at a signalized intersection were collected over a 2-month period. Logistic regression models calibrated to the data reveal that the acceptable gap duration decreases as a function of the drivers wait time and increases as the rain intensity increases. The study then demonstrates how the critical gap is influenced by a number of parameters, including the wait time and rain intensity, and also demonstrates how rain intensity affects the permissive left-turn saturation flow rate. It is anticipated that these findings can be used to develop weather-specific traffic signal timings that account for changes in traffic stream saturation flow rates due to changes in critical gap values as a function of weather conditions.

Impact of Inclement Weather on Left-Turn Gap Acceptance Behavior of Drivers

This paper describes an empirical study conducted to quantify the impact of a number of variables on left-turn gap acceptance behavior of drivers at signalized intersections. The variables include the gap duration, the travel time needed to cross the intersection, and the corresponding weather condition. The gap acceptance data set used in the study included 11,114 observations (1,176 accepted gaps and 9,938 rejected gaps) for a permitted left-turn maneuver at a signalized intersection; the data were gathered over 6 months. The data set was divided into six weather categories for different combinations of precipitation and roadway surface conditions. Logistic regression models were calibrated to the data and compared to identify the best model for capturing gap acceptance behavior of drivers. The models reveal that drivers are more conservative during snow than rain. Drivers require larger gaps for wet surface conditions than for snowy and icy surface conditions, and drivers require the smallest gaps for dry roadway conditions. In addition, the models show that drivers require larger gaps as the distance required to traverse the offered gap increases. The study also shows how inclement weather and the number of opposing lanes affect permitted left-turn saturation flow rates. It is anticipated that these findings will be used to develop weather-specific traffic signal timings that account for changes in traffic stream saturation flow rates and also used for intelligent assistance systems for drivers.

Framework for Intersection Decision Support in Adverse Weather Conditions

The impact of inclement weather on driver behavior can be significant and may lead to erroneous driver decisions. Consequently, the research being presented used sensor information to assist opposed left-turning vehicles at signalized intersections under various weather conditions. The research proposed a new framework for a real-time intersection decision support system for left-turning vehicles entitled IDS-W. The proposed system was tested with data from a signalized intersection in Christiansburg, Virginia. The intersection was equipped with four video cameras and an on-site weather station. Nine thousand fifty-eight observations covering various weather conditions were gathered. Each observation consisted of the driver decision (accept or reject), weather condition (dry, rain, or snow), illumination (day or night), gap size, and gap-offered location (lane number). The observed decisions and the corresponding gap variables were aggregated to build the IDS-W database, which was developed by fusing data from roadside sensors (input data) and provided a decision (accept or reject the offered gap) to the driver. A case-based reasoning approach was used to provide the gap acceptance decision. The approachs cycle starts with the entering of a new case (the combination of the gap size and the corresponding independent variables) and ends with the proposed decision (accept or reject the gap). The model was developed with 80% of the data to deduce the appropriate decision (accept or reject) and validated with the remaining 20% of the data through a Monte Carlo simulation. The results demonstrate the potential for delay reductions at the signalized intersection.

Survey on In-vehicle Technology Use: Results and Findings

ABSTRACT 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.

Agent-Based Framework for Modeling Gap Acceptance Behavior of Drivers Turning Left at Signalized Intersections

An algorithm based on a reactive driving agent was developed for modeling gap acceptance behavior of drivers making left turns at signalized intersections. The model considers the interaction between driver characteristics and the physical capabilities of vehicles. A vehicle dynamics model explicitly captures the vehicle constraints on driving behavior. In addition, the model uses the drivers input and psychological deliberation about accepting or rejecting a gap. The model was developed with a total of 301 accepted gaps and validated with 2,429 rejected gaps at the same site and with 1,485 gap decisions (323 accepted and 1,162 rejected) at another site. The proposed model is a mix of traditional and reactive methods for decision making and consists of three main components: input, data processing, and output. The input component uses sensing information and vehicle and driver characteristics to process the data and estimate the critical gap value. Then, the agent decides to either accept or reject the offered gap by comparing it 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 vehicle types, roadway configurations, traffic movements, intersection characteristics, and weather effects on driver gap acceptance behavior. The findings could be used to develop weather-specific traffic signal timing and in vehicle-to-vehicle communications.

Public perception on increasing use of technology in automobiles: Survey findings

The use of advanced technology in automobiles has increased dramatically over the past decade. Automobile manufacturers continue to equip vehicles with more in-vehicle devices, including: navigation systems, blind-spot assist systems and adaptive cruise control systems. At the same time, regulators such as the National Highway Traffic Safety Authority have been working to ensure that these systems do not pose a safety hazard. Opinions of the end-users regarding this increase in use of advanced in-vehicle technology have rarely been sought. Consequently, the research presented in this paper attempts to fill this gap using responses from a public opinion survey of 434 drivers. The results from this stated preference survey shows that over 89 percent of the respondents support advanced in-vehicle technologies whereas only 26 percent support technology use from an infrastructure side. The results from this paper are especially important as the connected vehicle program is being introduced to the research arena.

Enhancing Roundabout Operations via Vehicle Connectivity

With in-vehicle automation and vehicle connectivity gaining momentum, cooperative adaptive cruise control (CACC) systems are expected to enter the market in the near future. Given that the number of roundabouts in the United States has increased significantly, this research effort investigated the potential benefits of the use of CACC systems and vehicle-to-infrastructure connectivity to optimize the trajectories of vehicles approaching a single-lane roundabout. The optimization ensures that vehicles can enter the roundabout when gaps in the circulating roadway are available. The proposed idea is generally similar to the concept of metering single-lane entrance ramps. The system is simulated on a single-lane roundabout for different traffic demands and CACC market penetration levels. The study demonstrates that CACC systems can produce savings in total delay and fuel consumption levels of up to 80% and 40%, respectively, relative to the levels for traditional roundabout control. Further benefits are also achievable if one considers the potential to reduce the time headway between CACC-equipped vehicles and thus increase lane capacity.