Efficient mixed integer programming for autonomous overtaking

Molinari, Fabio; Anh, Nguyễn Ngọc; Re, Luigi del

doi:10.23919/acc.2017.7963296

Cited by 26 publications

(9 citation statements)

References 10 publications

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“…1. There are multiple ways of modeling these zones using a mixed integer approach (see for instance [10]). Here, the following binary variables are introduced for modelling the CZ and OW Variables:…”

Section: B Safety Constraintsmentioning

confidence: 99%

“…The straightforward way of formulating the optimal control problem is in the time domain using both continuous and binary variables to model the goals i-iii, i.e., a non-convex mixed integer program (MIP), [10]. The computation time of MIPs is highly dependent on choosing a feasible starting point, which might be difficult to find in complex traffic situations.…”

Section: Introductionmentioning

confidence: 99%

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Comparison between mixed-integer and second order cone programming for autonomous overtaking

Karlsson

Murgovski

Sjöberg

2018

2018 European Control Conference (ECC)

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This paper concerns optimally controlling an autonomous vehicle to perform safe and comfortable overtaking of a slower moving leading vehicle. The contribution is an analysis of the comparisons between a convex relaxation with the standard mixed integer quadratic program. The main difference between the formulations is that the sampling is performed in the temporal domain in the standard formulation, but in the spatial domain for the convex relaxation. The case of varying lateral position and longitudinal velocity is studied for both algorithms and the solution quality and computational effort are discussed. The results are given in a case study where an ego vehicle is forced to accelerate when overtaking a leading vehicle, due to the presence of an oncoming vehicle. The results illustrate that the temporal and spatial formulation's yield similar solutions but obtaining them require less computational effort in the spatial domain.

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Section: B Safety Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparison between mixed-integer and second order cone programming for autonomous overtaking

Karlsson

Murgovski

Sjöberg

2018

2018 European Control Conference (ECC)

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“…A stochastic model predictive control, in which the velocities of the surrounding vehicles are considered, is designed [4]. A mixed integer programming method, which uses a low number of variables, was presented by [5]. Another solution to reduce the numerical difficulties was to use linguistic variables.…”

Section: Introductionmentioning

confidence: 99%

Optimal Control of Overtaking Maneuver for Intelligent Vehicles

Németh

Gáspár

Hegedüs

2018

Journal of Advanced Transportation

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In the paper a hierarchical overtaking strategy, which is a driver assistance function or rather an autonomous function in electric/autonomous vehicles, is proposed. The solution uses speed and acceleration signals from the surrounding vehicles. These signals are processed with clustering methods in order to achieve probability density functions and predict their expected motion. The strategy includes several additional layers, such as decision making concerning the maneuver, the computation of the required trajectory, and the tracking control of the vehicle. Trajectory generation is formed as an optimization task, which is able to include the prediction model of the surrounding vehicles in the constraints. A robust Linear Parameter Varying (LPV) control design method is proposed to guarantee the tracking of the computed reference. The proposed strategy is able to guarantee the safe motion of the vehicles and handle the interactions with the other traffic participants.

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“…Many different strategies exist for solving motion planning tasks for autonomous vehicles: discretization-based planners, which include rapidly exploring random trees [8] and state lattices [27], discretize the search space by considering a finite set of possible motions only. Other approaches formulate motion planning as a continuous optimization problem [16,26], or precom- pute possible driving corridors to simplify the motion planning task [10,15]. Machine learning has also been successfully applied to motion planning of autonomous vehicles [21,25].…”

Section: Related Workmentioning

confidence: 99%

Verification of Collision Avoidance for CommonRoad Traffic Scenarios

Kochdumper¹,

Gassert²,

Matthias³

EPiC Series in Computing

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We propose a benchmark for the verification of autonomous vehicles. By considering different traffic scenarios from the CommonRoad database, we obtain several thousands of different verification tasks, where the verification problem is to prove that the con- sidered tracking controller safely follows a given reference trajectory despite disturbances and measurement errors. The dynamic of the car is described by a nonlinear kinematic single-track model. Since the feedback matrix for the tracking controller is time-varying, the dynamic of the controlled system changes constantly. Because of this, the proposed benchmark is well-suited to evaluate how robustly reachability tools can handle changing system dynamics.

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Efficient mixed integer programming for autonomous overtaking

Cited by 26 publications

References 10 publications

Comparison between mixed-integer and second order cone programming for autonomous overtaking

Comparison between mixed-integer and second order cone programming for autonomous overtaking

Optimal Control of Overtaking Maneuver for Intelligent Vehicles

Verification of Collision Avoidance for CommonRoad Traffic Scenarios

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