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Deep Learning Model Predictive Control for Autonomous Driving in Unknown Environments
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Vehicular Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-7349-1937
2018 (English)In: IFAC-PapersOnLine, E-ISSN 2405-8963, Vol. 51, no 22, p. 447-452Article in journal (Refereed) Published
Abstract [en]

A dynamic obstacle avoidance Model Predictive Control (MPC) method is introduced for autonomous driving that uses deep learning technique for velocity-dependent collision avoidance in unknown environments. The objective of the method is to control an autonomous vehicle in order to perform different traffic maneuvers in a safe way with maximum comfort of passengers, and in minimum possible time, accounting for maneuvering capabilities, vehicle dynamics, and in the presence of traffic rules, road boundaries and static and dynamic unknown obstacles. Here, by defining local coordinates and collision regions, the dynamic collision avoidance problem is translated into a static collision avoidance problem which makes the method easier and faster to be solved in dynamical environments. In order to provide safety, an ensemble of deep neural networks is used to estimate the probability of collision and to form an uncertainty-dependent collision cost which prioritizes between mission and safety. The collision cost is a product of the probability of collision and vehicle’s velocity in the directions with high collision-risk. The dynamic obstacle avoidance optimization method minimizes the velocity in the obstacle cones where the probability of collision is high or in unfamiliar environments, and increases the velocity when probability and variation in predicted values of the ensemble are low. The predicted trajectory from MPC is used in learning procedure in order to assign labels that makes it possible to predict the collision in advance. Simulation results show that the proposed method has good adaptability to unknown environments.

Place, publisher, year, edition, pages
2018. Vol. 51, no 22, p. 447-452
Keywords [en]
Autonomous vehicle, Deep learning, MPC, Neural networks, Optimization
National Category
Vehicle Engineering
Identifiers
URN: urn:nbn:se:liu:diva-172227DOI: 10.1016/j.ifacol.2018.11.593OAI: oai:DiVA.org:liu-172227DiVA, id: diva2:1512769
Available from: 2020-12-28 Created: 2020-12-28 Last updated: 2022-09-15Bibliographically approved
In thesis
1. Decentralized Optimal Control for Multiple Autonomous Vehicles in Traffic Scenarios
Open this publication in new window or tab >>Decentralized Optimal Control for Multiple Autonomous Vehicles in Traffic Scenarios
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

New transport technologies have the potential to create more efficient modes of transport and transforming cities for the better by improving urban productivity and increasing efficiency of its transport system to move consumers, labor, and freight. Traffic accidents, energy consumption, pollution, congestion, and long commuting times are main concerns and new transport technologies with autonomous vehicles have the potential to be part of the solution to these important challenges. 

An autonomous, or highly automated car is a vehicle that can operate with little to no human assistance. This technology is not yet generally available, but if fully realized have the potential to fundamentally change the transportation system. The passenger experience will fundamentally change, but there are also possibilities to increase traffic flow, form platoons of transport vehicles to reduce air-drag and thereby energy consumption, and a main challenge is to realize all this in a safe way in uncertain and complex traffic situations on highways and in urban scenarios. 

The key topic of this dissertation is how optimal control techniques, more specifically Model Predictive Control (MPC), can be applied in autonomous driving in dynamic environments and with dynamic constraints on vehicle behavior. The main problem studied is how to control multiple vehicles in an optimal, safe, and collision free way in complex traffic scenarios, e.g., laneswitching, merging, or intersection situations in the presence of moving obstacles, i.e., other vehicles whose behavior and intent may not be known. Further, the controller needs to take maneuvering capabilities of the vehicle into account, respecting road boundaries, speed limitations, and other traffic rules. Optimization-based techniques for control are interesting candidates for multi-vehicle problems, respecting well-defined rules in traffic while still providing a high degree of decision autonomy to each vehicle. 

To ensure autonomy, it is studied how to decentralize the control approach to not rely on a centralized computational resource. Different methods and approaches are proposed in the thesis with guaranteed convergence and collision-avoidance features. To reduce the computational complexity of the controller, a Gaussian risk model for collision prediction is integrated and also a technique that combines MPC with learning methods is explored. 

Main contributions of this dissertation are control methods for autonomous vehicles that provide safety and comfort of passengers even in uncertain traffic situations where the behavior of surrounding vehicles is uncertain, and the methods are computationally fast enough to be used in real time. An important property is that the proposed algorithms are general enough to be used in different traffic scenarios, hence reducing the need for specific solutions for specific situations. 

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2021. p. 31
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2116
National Category
Engineering and Technology Vehicle Engineering
Identifiers
urn:nbn:se:liu:diva-171829 (URN)9789179297152 (ISBN)
Public defence
2021-01-29, Ada Lovelace, B-Building, Campus Valla, Linköping, 10:15 (Swedish)
Opponent
Supervisors
Available from: 2020-12-28 Created: 2020-12-08 Last updated: 2021-12-28Bibliographically approved

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S2405896318333007

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Mohseni, FatemehVoronov, SergiiFrisk, Erik

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