Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.809896
Title: Driverless car and multiagent model predictive control
Author: Bali, Csaba
ISNI:       0000 0004 9346 9851
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2020
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Abstract:
The field of robotics has made autonomous vehicles a reality. Their wide-scale deployment is expected to revolutionize transportation as we know it by improving traffic efficiency, reducing the number of road accidents, and lowering transportation-related costs. Moreover, it will provide social groups that are currently unable to drive independently with the opportunity to experience the benefits of personal transportation. This work focuses on vehicle control at simple junctions in urban settings, challenging the limits of the optimal control technique of mixed-integer model predictive control. The challenging factor is the tendency for an exponentially growing number of potential discrete combinatorial choices to be considered as the number of discrete decisions (degree of freedom) in a problem increases. This imposes practical limitations on the number of vehicles, the length and resolution of future predictions, and the potential control configurations. Vehicle junction crossing orders are incorporated into the problem, in order to find the optimal crossing order with respect to vehicle dynamics, constraints, and relative priorities. Formulations are shown for merging at Y junctions, crossing at cross junctions, and box junctions to remove deadlock situations. Control policies are shown starting with globally optimal model predictive control, preserving safe vehicle interactions with intuitive, simple time-headway safety constraints providing a recursive feasible control technique. For comparison, heuristic first-come-first-served and soft pre-merging policies are also developed. Finally, simplifications of the mixed-integer formulations are shown for cross junctions to increase computational performance by exploiting the structure of the problem. The framework is further improved for future applications through added binary constraints and decentralised modification.
Supervisor: Richards, Arthur ; Piechocki, Robert Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.809896  DOI: Not available
Keywords: Model Predictive Control ; Driverless vehicles ; Junction control ; Mixed Integer Program ; Optimal Control
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