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TUTCRIS

Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Otsikko2018 European Control Conference (ECC)
KustantajaIEEE
Sivut641-648
Sivumäärä8
ISBN (elektroninen)978-3-9524-2698-2
ISBN (painettu)978-1-5386-5303-6
DOI - pysyväislinkit
TilaJulkaistu - kesäkuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Control Conference -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceEuropean Control Conference
Ajanjakso1/01/00 → …

Tiivistelmä

In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure where in first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem.We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.

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