Distributed Scenario Model Predictive Control for Driver Aided Intersection Crossing

Katriniok, Alexander; Kojchev, Stefan; Lefeber, Erjen; Nijmeijer, Henk

doi:10.23919/ecc.2018.8550526

Cited by 11 publications

(21 citation statements)

References 13 publications

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“…We assume that the driver does not change his general reaction over the prediction horizon. The deviation from the recommended speed, though, is considered to be time-varying over this interval, see [9]. To this end, we keep K…”

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

“…, N and every scenario κ ∈ K, we also bound the actual velocity and constrain the vehicle acceleration to accommodate physical vehicle limitations and safe driving. Following [9], we impose the lower bound v…”

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

“…Particularly, we claim that the prediction horizon at least covers the coordinate interval where potential collisions might occur with other agents. This way, convergence and feasibility of the distributed control scheme is ensured [9]. To ascertain collision avoidance, we follow our approach in [9].…”

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

“…This way, convergence and feasibility of the distributed control scheme is ensured [9]. To ascertain collision avoidance, we follow our approach in [9]. Essentially, collision avoidance constraints need to be satisfied jointly, thus requiring a certain consensus among agents.…”

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

“…Ultimately, only the agent with lower priority has to impose this safety constraint. With definition (11) of d [i] l,(k+j|k) , we can recast (19) in the form [9] (s…”

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

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A Stochastic Model Predictive Control Approach for Driver-Aided Intersection Crossing With Uncertain Driver Time Delay

Katriniok

Kojchev

Lefeber

et al. 2019

2019 18th European Control Conference (ECC)

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We investigate the problem of coordinating human-driven vehicles in road intersections without any traffic lights or signs by issuing speed advices. The vehicles in the intersection are assumed to move along an a priori known path and to be connected via vehicle-to-vehicle communication. The challenge arises with the uncertain driver reaction to a speed advice, especially in terms of the driver reaction time delay, as it might lead to unstable system dynamics. For this control problem, a distributed stochastic model predictive control concept is designed which accounts for driver uncertainties. By optimizing over scenarios, which are sequences of independent and identically distributed samples of the uncertainty over the prediction horizon, we can give probabilistic guarantees on constraint satisfaction. Simulation results demonstrate that the scenario-based approach is able to avoid collisions in spite of uncertainty while the non-stochastic baseline controller is not. A. Katriniok is with Ford Research

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Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

“…Ultimately, only the agent with lower priority has to impose this safety constraint. With definition (11) of d [i] l,(k+j|k) , we can recast (19) in the form [9] (s…”

Section: B Scenario Model Predictive Controlmentioning

confidence: 99%

See 3 more Smart Citations