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Interaction and Uncertainty-Aware Motion Planning for Autonomous Vehicles Using Model Predictive Control
Linköpings universitet, Institutionen för systemteknik, Fordonssystem. Linköpings universitet, Tekniska fakulteten.ORCID-id: 0000-0002-1050-3037
2023 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
Abstract [en]

Motion planning plays a significant role in enabling advances of autonomous vehicles in saving lives and improving traffic efficiency. In a predictive motion-planning strategy, the ego vehicle predicts the motion of surrounding vehicles and uses these predictions to plan collision-free reference trajectories. In dynamic multi-vehicle traffic environments, a key research question is how to consider vehicle-to-vehicle interactions and motion uncertainties of the surrounding vehicles in the motion planner to achieve resilient motion planning of the autonomous ego vehicle. 

This Licentiate Thesis proposes a model predictive control (MPC)-based approach to achieve safe motion planning in uncertain and dynamic multi-vehicle driving environments. MPC has been widely applied for the motion planning of autonomous vehicles. However, designing resilient MPC-based motion planners that consider interactions and uncertainties of surrounding vehicles remains an open and challenging problem, which is the primary motivation for the research presented in this thesis. 

This thesis makes several contributions toward solving the interaction and uncertainty-aware motion-planning problems. The first contribution is an MPC, which is called interaction-aware moving target MPC. It is designed based on the combination of an interaction-aware motion-prediction model and time-varying reference targets of the optimal control problem for proactive and non-local trajectory planning in multi-vehicle dynamic scenarios. 

In the second contribution, the proposed MPC is extended to account for the multi-modal motion uncertainties of surrounding vehicles, including the maneuver and trajectory uncertainties, which are predicted by combining an interaction-aware motion-prediction model and a data-driven approach. Based on the modeling of uncertainties, a safety-awareness parameter is included in the design to compute the obstacle occupancy for achieving a trade-off between the performance and robustness of the MPC planner. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset. 

The third contribution of this thesis is quantifying the motion uncertainty of surrounding obstacles to reduce the conservativeness of the motion planner while pursuing robustness. To this end, a robust motion-planning method is designed for robotic systems based on uncertainty quantification of surrounding obstacles. The proposed MPC is called risk-aware robust MPC, as the risk of robustness reduction through uncertainty quantification is analyzed. Simulations in highway merging scenarios of an autonomous vehicle with uncertain surrounding vehicles show that the approach is less conservative than a conventional robust MPC and more robust than a deterministic MPC.  

Abstract [sv]

Rörelseplanering spelar en betydande roll för att möjliggöra framsteg inom autonoma fordon med potential att rädda liv genom att undvika olyckor och förbättra trafikeffektiviteten. I en prediktiv rörelseplaneringsstrategi predikterar det egna fordonet rörelsen hos omgivande fordon och använder dessa prediktioner för att planera en säker trajektoria. I dynamiska trafiksituationer med multipla omgivande fordon är en central forsknings-fråga hur man ska ta hänsyn till de omgivande fordonens interaktioner och rörelseosäkerheter för att åstadkomma en robust rörelseplanering.

Den här licentiatavhandlingen föreslår en modellprediktiv reglerings-ansats (MPC) för rörelseplanering i osäkra och dynamiska flerfordonsmiljöer. Robust och säker modellprediktiv regleringsbaserad rörelseplanering som tar hänsyn till interaktioner och osäkerheter hos rörelsen för omgivande fordon är ett öppet och utmanande problem, vilket är den primära motiveringen för den forskning som presenteras i denna avhandling.

Modellprediktiv reglering (MPC) är en vanlig ansats för rörelseplanering för autonoma fordon. Denna avhandling presenterar metoder som är steg mot att lösa rörelseplaneringsproblemet där interaktion mellan fordon och osäkerhet i rörelser för omgivande fordon beaktas. Det första bidraget fokuserar på interaktionen mellan omgivande fordon. En modellprediktiv regulator har utvecklats baserat på en modell för hur omgivande fordon interagerar och påverkar varandras beteende. Denna modell integreras sedan som tidsvarierande referensmål för det optimala styrningsproblemet vilket ger en förutseende och robust planering för det egna fordonet.

I det andra bidraget utökas den föreslagna MPC-metoden för att ta hänsyn till de multimodala rörelseosäkerheterna hos omgivande fordon; det finns en osäkerhet i vad de omgivande fordonen kommer göra härnäst och det är osäkert hur de kommer genomföra det. Baserat på en modellering av osäkerheterna, en delvis datadriven ansats, inkluderas en säkerhetsparameter i regulatorn som möjliggör en avvägning mellan prestanda och robusthet hos MPC-planeraren.

Den tredje bidraget i avhandlingen är nya metoder för att kvantifiera rörelseosäkerheten hos omgivande fordon och att använda denna för robust planering, utan att det egna fordonet blir för konservativ i sitt agerande. Den föreslagna ansatsen bygger på robust MPC där en riskmedvetenhet introduceras. Simuleringar av motorvägskörning med omgivande fordon med rörelseosäkerhet visar att metoden är mindre konservativ än en konventionell robust MPC och mer robust än en deterministisk MPC.  

sted, utgiver, år, opplag, sider
Linköping: Linköping University Electronic Press, 2023. , s. 33
Serie
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1964
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-193646DOI: 10.3384/9789180752008ISBN: 9789180751995 (tryckt)ISBN: 9789180752008 (digital)OAI: oai:DiVA.org:liu-193646DiVA, id: diva2:1756307
Presentation
2023-06-13, Ada Lovelace, B-building, Campus Valla, Linköping, 10:15 (engelsk)
Opponent
Veileder
Merknad

Funding: This research was supported by the Strategic Research Area at Linköping-Lund in Information Technology (ELLIIT).

Tilgjengelig fra: 2023-05-11 Laget: 2023-05-11 Sist oppdatert: 2023-05-11bibliografisk kontrollert
Delarbeid
1. Interaction-Aware Moving Target Model Predictive Control for Autonomous Vehicles Motion Planning
Åpne denne publikasjonen i ny fane eller vindu >>Interaction-Aware Moving Target Model Predictive Control for Autonomous Vehicles Motion Planning
2022 (engelsk)Inngår i: 2022 EUROPEAN CONTROL CONFERENCE (ECC), IEEE , 2022, s. 154-161Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

This paper investigates an integrated traffic environment modeling and model predictive control (MPC) system to realize interaction-aware dynamic motion planning of an autonomous vehicle with multiple surrounding vehicles. The interaction-aware interacting multiple model Kalman filter (IAIMM-KF) from the literature is used to hierarchically predict maneuvers and trajectories of surrounding vehicles and to compute safe targets for the ego vehicle. The targets are terminal speed and reference lane, which are moving targets as they are updated at each time step. Then, an MPC controller is designed for the ego vehicle to generate an optimal trajectory by following the moving targets and including the prediction results to formulate collision-free constraints. The proposed interaction-aware planning method has a proactive planning ability and can avoid collisions by non-local replanning. The strengths and effectiveness of the approach are verified in challenging highway lane-change simulation scenarios.

sted, utgiver, år, opplag, sider
IEEE, 2022
HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-189502 (URN)10.23919/ECC55457.2022.9838002 (DOI)000857432300019 ()9783907144077 (ISBN)9781665497336 (ISBN)
Konferanse
European Control Conference (ECC), London, ENGLAND, jul 12-15, 2022
Merknad

Funding Agencies|Strategic Research Area at Linkoping-Lund in Information Technology (ELLIIT); Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Tilgjengelig fra: 2022-10-25 Laget: 2022-10-25 Sist oppdatert: 2023-05-11
2. Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainties Using Model Predictive Control
Åpne denne publikasjonen i ny fane eller vindu >>Interaction-Aware Motion Planning for Autonomous Vehicles with Multi-Modal Obstacle Uncertainties Using Model Predictive Control
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

This paper proposes an interaction-aware motion-planning method for an autonomous vehicle in uncertain multi-vehicle traffic environments. An interaction-aware motion-prediction model is used to predict the behaviors of surrounding vehicles. The multi-modal prediction uncertainties, containing both the maneuver and trajectory uncertainties of surrounding vehicles, are considered in the method for resilient motion planning of the ego vehicle. Based on the prediction of the surrounding vehicles, an optimal reference trajectory of the ego vehicle is computed by model predictive control (MPC) to follow the time-varying reference targets and avoid collisions with obstacles. A trade-off between the performance and robustness of the method can be achieved by tuning a safety-awareness parameter in the MPC. The efficiency of the method is illustrated in challenging highway-driving simulation scenarios and a driving scenario from a recorded traffic dataset.

HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-193650 (URN)10.48550/arXiv.2212.11819 (DOI)
Tilgjengelig fra: 2023-05-11 Laget: 2023-05-11 Sist oppdatert: 2023-05-16bibliografisk kontrollert
3. Robust Tube Model Predictive Control with Uncertainty Quantification for Discrete-Time Linear Systems
Åpne denne publikasjonen i ny fane eller vindu >>Robust Tube Model Predictive Control with Uncertainty Quantification for Discrete-Time Linear Systems
Vise andre…
(engelsk)Manuskript (preprint) (Annet vitenskapelig)
Abstract [en]

This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and hard constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an MPC algorithm incorporating online uncertainty quantification that builds on prior knowledge of the disturbance, i.e., a known but conservative disturbance set. We approximate the true disturbance set at each time step with a parameterised set, which is referred to as a quantified disturbance set, using the scenario approach with additional disturbance realisations collected online. A key novelty of this paper is that the parameterisation of these quantified disturbance sets enjoy desirable properties such that the quantified disturbance set and its corresponding rigid tube bounding disturbance propagation can be efficiently updated online. We provide statistical gaps between the true and quantified disturbance sets, based on which, probabilistic recursive feasibility of MPC optimisation problems are discussed. Numerical simulations are provided to demonstrate the efficacy of our proposed algorithm and compare with conventional robust MPC algorithms.

HSV kategori
Identifikatorer
urn:nbn:se:liu:diva-193651 (URN)10.48550/arXiv.2304.05105 (DOI)
Merknad

Funding: This work is supported by the Knut and Alice Wallenberg Foundation, the Swedish Strategic Research Foundation and the Swedish Research Council.

Tilgjengelig fra: 2023-05-11 Laget: 2023-05-11 Sist oppdatert: 2023-05-11

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