The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles

Englund, Cristofer; Chen, Lei; Ploeg, Jeroen; Semsar-Kazerooni, Elham; Voronov, Alexey; Bengtsson, Hoai Hoang; Didoff, Jonas

doi:10.1109/mwc.2016.7553038

Cited by 114 publications

(54 citation statements)

References 13 publications

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“…In this sense, during recent years many works have addressed this topic, trying to provide solutions based on different configuration and solutions. The Grand Cooperative Driving Challenge 2016 was created with the purpose of boosting cooperative automated vehicles in the form of a cooperative based competition [19]. On the one hand, authors in [20] presented an auction-based cooperative control for autonomous vehicles; on the other hand, authors in [21] proposed an approach to enhance common motion planning algorithms; this proposal allows cooperation with human-driven vehicles.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hussein

Marín-Plaza

García

et al. 2018

Journal of Advanced Transportation

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Self-driving cars are attracting significant attention during the last few years, which makes the technology advances jump fast and reach a point of having a number of automated vehicles on the roads. Therefore, the necessity of cooperative driving for these automated vehicles is exponentially increasing. One of the main issues in the cooperative driving world is the Multirobot Task Allocation (MRTA) problem. This paper addresses the MRTA problem, specifically for the problem of vehicles and requests allocation. The objective is to introduce a hybrid optimization-based approach to solve the problem of multiple intelligent vehicles requests allocation as an instance of MRTA problem, to find not only a feasible solution, but also an optimized one as per the objective function. Several test scenarios were implemented in order to evaluate the efficiency of the proposed approach. These scenarios are based on well-known benchmarks; thus a comparative study is conducted between the obtained results and the suboptimal results. The analysis of the experimental results shows that the proposed approach was successful in handling various scenarios, especially with the increasing number of vehicles and requests, which displays the proposed approach efficiency and performance.

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Section: Related Workmentioning

confidence: 99%

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hussein

Marín-Plaza

García

et al. 2018

Journal of Advanced Transportation

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“…We choose the coefficients in (4) with d i = 0.5 and h i = 1 according to the results in [41]. In addition, the initial positions and velocities of the vehicles in the platoon are designed as r L (0) = 12, r i (0) = [11,9,7,6,4,2,0] and v L (0) = 0, v i (0) = 0.…”

Section: Simulation Setupmentioning

confidence: 99%

“…Vehicle platoon control has received substantially increasing interest from many institutions, such as the program of the Partners for Advanced Transit and Highways (PATH) in California [1], the Grand Cooperative Driving Challenge (GCDC) in Netherlands [2], Safe Road Trains for the Environment (SARTRE) in Europe [3] and Energy-ITSin Japan [4]. It has many advantages for road traffic, e.g., reducing fuel consumption (potentially up to 20%), enhancing traffic safety (anticipated 10% reduction in fatalities), as well as increasing driver convenience (autonomous systems for following vehicles) [3].…”

Section: Introductionmentioning

confidence: 99%

Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback

et al. 2017

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This paper investigates the output feedback control problem of a vehicle platoon with a constant time headway (CTH) policy, where each vehicle can communicate with its consecutive vehicles. Firstly, based on the integrated-sliding-mode (ISM) technique, a neural adaptive sliding-mode control algorithm is developed to ensure that the vehicle platoon is moving with the CTH policy and full state measurement. Then, to further decrease the measurement complexity and reduce the communication load, an output feedback control protocol is proposed with only position information, in which a higher order sliding-mode observer is designed to estimate the other required information (velocities and accelerations). In order to avoid collisions among the vehicles, the string stability of the whole vehicle platoon is proven through the stability theorem. Finally, numerical simulation results are provided to verify its effectiveness and advantages over the traditional sliding-mode control method in vehicle platoons.

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“…The main objective of the GCDC 2016 was to provide a basis for development of cooperative automated driving applications in an international context. To this end, a multi-brand approach was adopted, where vehicles from different manufacturers can cooperate based on a minimum set of common rules such as safety regulations and communication protocols [26]. In particular, the GCDC aimed to involve multiple parties from academia and industry to jointly develop and implement cooperative driving applications, while exploring the required functionality regarding vehicle automation and interaction protocols.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge

Ploeg

Semsar-Kazerooni

Medina

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-Cooperative adaptive cruise control and platooning are well-known applications in the field of cooperative automated driving. However, extension towards maneuvering is desired to accommodate common highway maneuvers, such as merging, and to enable urban applications. To this end, a layered control architecture is adopted. In this architecture, the tactical layer hosts the interaction protocols, describing the wireless information exchange to initiate the vehicle maneuvers, supported by a novel wireless message set, whereas the operational layer involves the vehicle controllers to realize the desired maneuvers. This hierarchical approach was the basis for the Grand Cooperative Driving Challenge (GCDC), which was held in May 2016 in The Netherlands. The GCDC provided the opportunity for participating teams to cooperatively execute a highway lanereduction scenario and an urban intersection-crossing scenario. The GCDC was set up as a competition and, hence, also involving assessment of the teams' individual performance in a cooperative setting. As a result, the hierarchical architecture proved to be a viable approach, whereas the GCDC appeared to be an effective instrument to advance the field of cooperative automated driving.Index Terms-Cooperative driving, interaction protocol, controller design, vehicle platoons, wireless communications. A. Voronov and C. Englund are with RISE Viktoria,

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The Grand Cooperative Driving Challenge 2016: boosting the introduction of cooperative automated vehicles

Cited by 114 publications

References 13 publications

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Hybrid Optimization-Based Approach for Multiple Intelligent Vehicles Requests Allocation

Neural Adaptive Sliding-Mode Control of a Vehicle Platoon Using Output Feedback

Cooperative Automated Maneuvering at the 2016 Grand Cooperative Driving Challenge

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