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Safe Stop Trajectory Planning for Highly Automated Vehicles:An Optimal Control Problem Formulation
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems. (Autonomous Driving)ORCID iD: 0000-0001-6492-1966
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Embedded Control Systems.ORCID iD: 0000-0001-9314-545x
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
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2018 (English)In: 2018 IEEE Intelligent Vehicles Symposium (IV), Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 517-522, article id 8500536Conference paper, Published paper (Refereed)
Abstract [en]

Highly automated road vehicles need the capabilityof stopping safely in a situation that disrupts continued normaloperation, e.g. due to internal system faults. Motion planningfor safe stop differs from nominal motion planning, since thereis not a specific goal location. Rather, the desired behavior isthat the vehicle should reach a stopped state, preferably outsideof active lanes. Also, the functionality to stop safely needs tobe of high integrity. The first contribution of this paper isto formulate the safe stop problem as a benchmark optimalcontrol problem, which can be solved by dynamic programming.However, this solution method cannot be used in real-time. Thesecond contribution is to develop a real-time safe stop trajectoryplanning algorithm, based on selection from a precomputedset of trajectories. By exploiting the particular properties ofthe safe stop problem, the cardinality of the set is decreased,making the algorithm computationally efficient. Furthermore, amonitoring based architecture concept is proposed, that ensuresdependability of the safe stop function. Finally, a proof of conceptsimulation using the proposed architecture and the safe stoptrajectory planner is presented.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 517-522, article id 8500536
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-232815DOI: 10.1109/IVS.2018.8500536Scopus ID: 2-s2.0-85056784560ISBN: 9781538644522 (print)OAI: oai:DiVA.org:kth-232815DiVA, id: diva2:1236532
Conference
2018 IEEE Intelligent Vehicles Symposium, IV 2018; Changshu, Suzhou; China; 26 September 2018 through 30 September 2018
Note

QC 20180806

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2019-05-22Bibliographically approved

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Svensson, LarsMohan, NaveenWard, ErikPernestål Brenden, AnnaFeng, LeiTörngren, Martin

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