The Experts below are selected from a list of 533160 Experts worldwide ranked by ideXlab platform
Nan Cheng - One of the best experts on this subject based on the ideXlab platform.
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TLB-VTL: 3-Level Buffer Based Virtual Traffic Light Scheme for Intelligent Collaborative Intersections
2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), 2017Co-Authors: Gaochao Wang, Yanyu Zhang, Yi Zhou, Ning Lu, Nan ChengAbstract:To improve the safety, traffic efficiency, and fairness among vehicles at Intersections, it is urgent to study intelligent collaborative strategies and make Intersections smarter. In this paper, a 3-Level Buffer (TLB) based Virtual Traffic Light (VTL) scheme, named TLB-VTL, is proposed for intelligent collaborative Intersections. The intersection is divided into three adaptive areas according to the traffic flow of each lane, and the sequence of each timing cycle is calculated in realtime according to the flow in TLB around an intersection. To ensure fairness, the difference in probability of each lane to pass an intersection is restricted to a lower level. The VTL is realized based on communications of vehicle-to-vehicle (V2V), vehicle-to-roadside (V2R), and vehicle-to- infrastructure (V2I), which could improve the safety and fairness without involving traffic lights. Moreover, a Cooperative Collision Avoidance Predictive control (CCAP) algorithm is proposed, which can assist vehicles to go across the next intersection without stopping through predicting the time conflict and generating an efficient traffic schedule for the entire road network. The simulation results indicate that the proposed TLB-VTL algorithm improves the fairness by 331%, decreases the average delay by 88%, and improves the ability to solve congestion by 12% compared with the traditional traffic light algorithm. Besides, the CCAP algorithm increases the traffic fluency by 45% at the intersection.
Priyantha Mudalige - One of the best experts on this subject based on the ideXlab platform.
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RTCSA - Ballroom Intersection Protocol: Synchronous Autonomous Driving at Intersections
2015 IEEE 21st International Conference on Embedded and Real-Time Computing Systems and Applications, 2015Co-Authors: Reza Azimi, Ragunathan Rajkumar, Gaurav Bhatia, Priyantha MudaligeAbstract:Road Intersections are considered to be serious bottlenecks in urban transportation. More than 44% of all reported crashes in U.S. Occur within intersection areas, which in turn lead to 8,500 fatalities and approximately 1 million injuries every year. Furthermore, because traffic traveling in one direction is generally stopped at busy Intersections to allow traffic to flow in another direction, an intersection creates traffic congestion and frustration. The impact of road Intersections on traffic delays leads to enormous waste of human and natural resources. According to the 2011 Urban Mobility Report, the delay endured by the average commuter was 34 hours, which costs in aggregate more than $100 billion each year in the U.S. With the advances in Cyber-Physical Systems (CPS), autonomous driving as a part of Intelligent Transportation Systems (ITS) is likely to be at the heart of urban transportation in the future. Autonomous vehicles have been demonstrated successfully at the DARPA Urban Challenge. General Motors' Electrical-Networked Vehicle, CMU's autonomous vehicle and Google's car are just a few other recently unveiled examples. Therefore, it is critical to address safety and throughput concerns as one of the main challenges for autonomous driving through Intersections. In this paper, we propose a spatio-temporal technique called the Ballroom Intersection Protocol (BRIP) to manage the safe and efficient passage of autonomous vehicles through Intersections. To achieve high throughput at Intersections, BRIP aims to maximize the utilization of the capacity of the intersection area by increasing parallelism. By enforcing a synchronized arrival of autonomous vehicles at Intersections, BRIP allows vehicles approaching from all directions to simultaneously and continuously cross without stopping behind or inside the intersection area. Our simulation results show that we are able to avoid collisions and increase the throughput of the Intersections by up to 96.24% compared to common signalized Intersections. Under BRIP, the optimal intersection capacity utilization of 100% is achievable in certain cases.
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STIP: Spatio-temporal intersection protocols for autonomous vehicles
2014 ACM IEEE International Conference on Cyber-Physical Systems ICCPS 2014, 2014Co-Authors: Reza Azimi, Ragunathan Raj Rajkumar, Gaurav Bhatia, Priyantha MudaligeAbstract:Autonomous driving is likely to be the heart of urban transportation in the future. Autonomous vehicles have the potential to increase the safety of passengers and also to make road trips shorter and more enjoyable. As the first steps toward these goals, many car manufacturers are investing in designing and equipping their vehicles with advanced driver-assist systems. Road Intersections are considered to be serious bottlenecks of urban transportation, as more than 44% of all reported crashes in U.S. occur within intersection areas which in turn lead to 8,500 fatalities and approximately 1 million injuries every year. Furthermore, the impact of road Intersections on traffic delays leads to enormous waste of human and natural resources. In this paper, we therefore focus on intersection management in Intelligent Transportation Systems (ITS) research. In the future, when dealing with autonomous vehicles, it is critical to address safety and throughput concerns that arise from autonomous driving through Intersections and roundabouts. Our goal is to provide vehicles with a safe and efficient passage method through Intersections and roundabouts. We have been investigating vehicle-to-vehicle (V2V) communications as a part of co-operative driving in the context of autonomous driving. We have designed and developed efficient and reliable intersection protocols to avoid vehicle collisions at Intersections and increase traffic throughput. In this paper, we introduce new V2V intersection protocols to achieve the above goals. We show that, in addition to Intersections, these protocols are also applicable to vehicle crossings at roundabouts. Additionally, we study the effects of position inaccuracy of commonly-used GPS devices on some of our V2V intersection protocols and suggest required modifications to guarantee their safety and efficiency despite these impairments. Our simulation results show that we are able to avoid collisions and also increase the throughput of the interse- tions up to 87.82% compared to common traffic-light signalized Intersections.
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V2V-Intersection Management at Roundabouts
SAE International Journal of Passenger Cars - Mechanical Systems, 2013Co-Authors: Reza Azimi, Ragunathan Raj Rajkumar, Gaurav Bhatia, Raj Rajkumar, Priyantha MudaligeAbstract:More than 44% of all automotive crashes occur in Intersections. These incidents in Intersections result in more than 8,500 fatalities and approximately 1 million injuries each year in USA. It is also established that roundabouts are safer than junctions. According to a USDOT study, when compared with the junctions they replaced, roundabouts have 40% fewer vehicle collisions, 80% fewer injuries and 90% fewer serious injuries and fatalities. In earlier work, we have proposed a family of vehicular network protocols, which use Dedicated Short Range Communications (DSRC) and Wireless Access in Vehicular Environment (WAVE) technologies to coordinate a vehicle's movement through Intersections. We have shown that vehicle-to-vehicle (V2V) communications can be used to avoid collisions at the intersection and also significantly decrease the trip delays introduced by traffic lights and stop signs. In this paper, we investigate the use of our proposed V2V-intersection protocols for autonomous driving at roundabouts. We have extended our hybrid emulator-simulator called AutoSim to implement realistic map and mobility models to study traffic flow at roundabouts and have implemented our V2V-intersection protocols on roundabouts. Using a simulated environment, we quantify the benefits of our proposed intersection protocols in terms of safety and throughput enhancements while negotiating roundabouts. 2013 SAE International.
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Reliable intersection protocols using vehicular networks
2013 ACM IEEE International Conference on Cyber-Physical Systems ICCPS 2013, 2013Co-Authors: Soheil Azimi, Ragunathan Raj Rajkumar, Seyed Azimi, Ragunathan Rajkumar, Gaurav Bhatia, Priyantha MudaligeAbstract:Autonomous driving will play an important role in the future of transportation. Various autonomous vehicles have been demonstrated at the DARPA Urban Challenge [3]. General Motors has recently unveiled their Electrical-Networked Vehicles (EN-V) in Shanghai, China [5]. One of the main challenges of autonomous driving in urban areas is transition through cross-roads and Intersections. In addition to safety concerns, current intersection management technologies such as stop signs and traffic lights can introduce significant traffic delays even under light traffic conditions. Our goal is to design and develop efficient and reliable intersection protocols to avoid vehicle collisions at Intersections and increase the traffic throughput. The focus of this paper is investigating vehicle-to-vehicle (V2V) communications as a part of co-operative driving in the context of autonomous vehicles. We study how our proposed V2V intersection protocols can be beneficial for autonomous driving, and show significant improvements in throughput. We also prove that our protocols avoid deadlock situations inside the intersection area. The simulation results show that our new proposed V2V intersection protocols provide both safe passage through the intersection and significantly decrease the delay at the intersection and our latest V2V intersection protocol yields over 85 % overall performance improvement over the common traffic light models.
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vehicular networks for collision avoidance at Intersections
SAE International Journal of Passenger Cars - Electronic and Electrical Systems, 2011Co-Authors: Seyed Azimi, Ragunathan Rajkumar, Gaurav Bhatia, Priyantha MudaligeAbstract:A substantial fraction of automotive collisions occur at Intersections. Statistics collected by the Federal Highway Administration (FHWA) show that more than 2.8 million intersection-related crashes occur in the United States each year, with such crashes constituting more than 44 percent of all reported crashes [12]. In addition, there is a desire to increase throughput at Intersections by reducing the delay introduced by stop signs and traffic signals. In the future, when dealing with autonomous vehicles, some form of co-operative driving is also necessary at Intersections to address safety and throughput concerns. In this paper, we investigate the use of vehicle-to-vehicle (V2V) communications to enable the navigation of traffic Intersections, to mitigate collision risks, and to increase intersection throughput significantly. Specifically, we design a vehicular network protocol that integrates with mobile wireless radio communication standards such as Dedicated Short Range Communications (DSRC) and Wireless Access in a Vehicular Environment (WAVE). This protocol relies primarily on using V2V communications, GPS and other automotive sensors to safely navigate Intersections and also to enable autonomous vehicle control. Vehicles use DSRC/WAVE wireless media to periodically broadcast their position information along with the driving intentions as they approach Intersections. We used the hybrid simulator called GrooveNet [1, 2] in order to study different driving scenarios at Intersections using simulated vehicles interacting with each other. Our simulation results indicate that very reasonable improvements in safe throughput are possible across many practical traffic scenarios.
Francesco Borrelli - One of the best experts on this subject based on the ideXlab platform.
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Intelligent Vehicles Symposium - A Bayesian filter for modeling traffic at stop Intersections
2015 IEEE Intelligent Vehicles Symposium (IV), 2015Co-Authors: Thierry Wyder, Georg Schildbach, Stephanie Lefevre, Francesco BorrelliAbstract:All-way stop Intersections are widely used for traffic management in North America. Therefore, modeling and control of vehicle behavior at stop Intersections is fundamental for driver assistance systems and autonomous driving. This paper presents a method to predict the maneuvers performed by vehicles at arbitrary all-way stop Intersections, using noisy sensor data. This is required for an autonomous vehicle to decide when to enter the intersection, or for a driver assistance system to decide when to issue a collision warning to the driver. The problem is divided into two components. The first component estimates the maneuver intention of the drivers by means of a naive Bayesian filter. The second component predicts the order in which the vehicles will enter the intersection by means of a kinematic feedback model. Both algorithms are evaluated using real world data collected with laser sensors mounted on a vehicle. The Bayesian filter is successfully applied to Intersections of different sizes and geometries. We show that the filter identifies maneuvers earlier than a deterministic reference model.
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A Bayesian filter for modeling traffic at stop Intersections
2015 IEEE Intelligent Vehicles Symposium (IV), 2015Co-Authors: Thierry Wyder, Georg Schildbach, Stephanie Lefevre, Francesco BorrelliAbstract:All-way stop Intersections are widely used for traffic management in North America. Therefore, modeling and control of vehicle behavior at stop Intersections is fundamental for driver assistance systems and autonomous driving. This paper presents a method to predict the maneuvers performed by vehicles at arbitrary all-way stop Intersections, using noisy sensor data. This is required for an autonomous vehicle to decide when to enter the intersection, or for a driver assistance system to decide when to issue a collision warning to the driver. The problem is divided into two components. The first component estimates the maneuver intention of the drivers by means of a naïve Bayesian filter. The second component predicts the order in which the vehicles will enter the intersection by means of a kinematic feedback model. Both algorithms are evaluated using real world data collected with laser sensors mounted on a vehicle. The Bayesian filter is successfully applied to Intersections of different sizes and geometries. We show that the filter identifies maneuvers earlier than a deterministic reference model.
Ardalan Vahidi - One of the best experts on this subject based on the ideXlab platform.
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Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming
2018 Annual American Control Conference (ACC), 2018Co-Authors: Faraz Ashtiani, Alireza S Fayazi, Ardalan VahidiAbstract:This paper extends our previous work in [1] and [2] on optimal scheduling of autonomous vehicle arrivals at Intersections, from one to a grid of Intersections. A scalable distributed Mixed Integer Linear Program (MILP) is devised that solves the scheduling problem for a grid of Intersections. A computational control node is allocated to each intersection and regularly receives position and velocity information from subscribed vehicles. Each node assigns an intersection access time to every subscribed vehicle by solving a local MILP. Neighboring Intersections will coordinate with each other in real-time by sharing their solutions for vehicles' access times with each other. Our proposed approach is applied to a grid of Intersections and its positive impact on traffic flow and vehicles' fuel economy is demonstrated in comparison to conventional intersection control scenarios.
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Optimal scheduling of autonomous vehicle arrivals at intelligent Intersections via MILP
Proceedings of the American Control Conference, 2017Co-Authors: S. Alireza Fayazi, Alireza S Fayazi, Ardalan Vahidi, Andre LuckowAbstract:We propose optimal scheduling of autonomous vehicle arrivals at Intersections, eliminating the need for physi- cal traffic signals. The proposed intersection control algorithm is assumed to have bi-directional communication links to approaching vehicles. After receiving subscription requests and status of approaching vehicles, the intersection control node cal- culates an arrival schedule that ensures safety while significantly reducing number of stops and intersection delays. The vehicle- intersection coordination problem is formulated as a Mixed- Integer Linear Program (MILP). A case study is presented and a customized traffic microsimulation environment is developed to demonstrate the effectiveness of the proposed intersection control scheme. I.
C. K. Wong - One of the best experts on this subject based on the ideXlab platform.
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Designs for Safer Signal-Controlled Intersections by Statistical Analysis of Accident Data at Accident Blacksites
IEEE Access, 2019Co-Authors: C. K. WongAbstract:This paper describes the collection and statistical analysis of accident counts and intersection layout geometries at a range of signal-controlled Intersections, with the aim of improving safety at these sites. Negative binomial regression analysis is conducted to relate the accident count data as a dependent variable, with various independent variables to capture the intersection layout and lane-marking patterns. Statistically significant variables are identified, and their individual effects on accident counts are analyzed. Although the accident-prediction models for signalized Intersections have been extensively investigated, this paper also considers the effects of shared lane markings, which is a new approach. The results of this paper show that the shared lane markings are indeed a statistically significant predictor of the number of accidents. It was found that the accident counts at signal-controlled Intersections could be reduced by altering the lane-marking patterns using a combination of well-established lane-based design methods and new governing constraint sets to enhance the safety controls for turning traffic derived from our statistical analysis. These new lane-marking patterns also satisfy engineering performance requirements. The Intersections in Hong Kong were investigated as illustrative case studies, and the numerical results show a substantial decrease in the predicted accident counts, with an acceptable tradeoff in the reduction of overall intersection capacity.
