Experimental testing of a semi-autonomous multi-vehicle collision avoidance algorithm at an intersection testbed

Ahn, Heejin; Rizzi, Andrea; Colombo, Alessandro; Vecchio, Domitilla Del

doi:10.1109/iros.2015.7354056

Cited by 19 publications

(10 citation statements)

References 24 publications

(27 reference statements)

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“…In particular, for second-order linear dynamics, the job-shop scheduling problem may be reformulated as a mixed-integer quadratic programming problem. Also, as considered in [5,1,2] in which an intersection is modeled as a single conflict area, measurement and process uncertainty and the presence of unequipped vehicles will be investigated in future work. Undetermined routes of vehicles will also be investigated by considering possible decisions of steering inputs.…”

Section: Discussionmentioning

confidence: 99%

Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

Ahn

Vecchio

2016

Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control

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In this paper, we design a supervisor to prevent vehicle collisions at intersections. An intersection is modeled as an area containing multiple conflict points where vehicle paths cross in the future. At every time step, the supervisor determines whether there will be more than one vehicle in the vicinity of a conflict point at the same time. If there is, then an impending collision is detected, and the supervisor overrides the drivers to avoid collision. A major challenge in the design of a supervisor as opposed to an autonomous vehicle controller is to verify whether future collisions will occur based on the current drivers choices. This verification problem is particularly hard due to the large number of vehicles often involved in intersection collision, to the multitude of conflict points, and to the vehicles dynamics. In order to solve the verification problem, we translate the problem to a job-shop scheduling problem that yields equivalent answers. The job-shop scheduling problem can, in turn, be transformed into a mixed-integer linear program when the vehicle dynamics are first-order dynamics, and can thus be solved by using a commercial solver.

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Section: Discussionmentioning

confidence: 99%

Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

Ahn

Vecchio

2016

Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control

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“…Basically, there are approaches that employ centralized control, by implementing intersection management unit which collects data from all participating vehicles and then calculates best speed profiles and assigns priorities: [10] implements supervisor which receives state information from the cars and then estimates possible collisions and sends overriding signal if necessary, [11] presents reservation-based intersection control algorithm. Others approaches include distributed intersection management, e.g.…”

Section: State Of the Artmentioning

confidence: 99%

“…[16] uses job-shop scheduling. Centralized V2I coordination that employs Active set method and Interior point method for optimization is used in [17]. In [18] and [19] genetic optimization of fuzzy controller are used to achieve better results.…”

Section: State Of the Artmentioning

confidence: 99%

Cooperative Intelligence in Roundabout Intersections Using Hierarchical Fuzzy Behavior Calculation of Vehicle Speed Profile

Bosankic¹,

Mehmedović

2016

MATEC Web Conf.

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Abstract. In this paper, a new fuzzy-behavior-based algorithm for roundabout intersection management is presented. The algorithm employs cooperative intelligence and includes intelligent vehicles and infrastructure to calculate speed profiles for different vehicles, in order to achieve more comfortable driving profiles, as well to reduce congestion and CO2 emissions. The algorithm uses adaptive spatio-temporal reservation technique and was tested in MATLAB/Simulink environment. The algorithm is designed to function in different scenarios with both cooperative and non-cooperative vehicles, as well as optional intersection infrastructure. Results have show that using the proposed algorithm different vehicle communication types can be successfully combined in order to increase traffic flow through roundabout intersections.

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“…We validate the supervisors by performing computer simulations and lab-based experimental testing. Some of these experimental results were presented in Ahn et al (2015). This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Robust Supervisors for Intersection Collision Avoidance in the Presence of Legacy Vehicles

Ahn

Rizzi

Colombo

2019

Int. J. Control Autom. Syst.

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We present the design and validation of a centralized controller, called a supervisor, for collision avoidance of multiple human-driven vehicles at a road intersection, considering measurement errors, unmodeled dynamics, and uncontrolled vehicles. We design the supervisor to be least restrictive, that is, to minimize its interferences with human drivers. This performance metric is given a precise mathematical form by splitting the design process into two subproblems: verification problem and supervisor-design problem. The verification problem determines whether an input signal exists that makes controlled vehicles avoid collisions at all future times. The supervisor is designed such that if the verification problem returns yes, it allows the drivers' desired inputs; otherwise, it overrides controlled vehicles to prevent collisions. As a result, we propose exact and efficient supervisors. The exact supervisor solves the verification problem exactly but with combinatorial complexity. In contrast, the efficient supervisor solves the verification problem within a quantified approximation bound in polynomially bounded time with the number of controlled vehicles. We validate the performances of both supervisors through simulation and experimental testing. Journal Title XX(X)Assumption 4. The measurement noise is bounded, that is,The aggregate state measurement is denoted by x m (t), and then, x(t) = x m (t) + δ, where δ ∈ ∆ := [δ min , δ max ] is the aggregated measurement noise.

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Experimental testing of a semi-autonomous multi-vehicle collision avoidance algorithm at an intersection testbed

Cited by 19 publications

References 24 publications

Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

Semi-autonomous Intersection Collision Avoidance through Job-shop Scheduling

Cooperative Intelligence in Roundabout Intersections Using Hierarchical Fuzzy Behavior Calculation of Vehicle Speed Profile

Robust Supervisors for Intersection Collision Avoidance in the Presence of Legacy Vehicles

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