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Publications (10 of 65) Show all publications
Kokogias, S., Svensson, L., Pereira, G. C., Oliveira, R., Zhang, X., Song, X. & Mårtensson, J. (2018). Development of Platform-Independent System for Cooperative Automated Driving Evaluated in GCDC 2016. IEEE transactions on intelligent transportation systems (Print), 19(4), 1277-1289
Open this publication in new window or tab >>Development of Platform-Independent System for Cooperative Automated Driving Evaluated in GCDC 2016
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2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 4, p. 1277-1289Article in journal (Refereed) Published
Abstract [en]

Cooperative automated driving is a promising development in reducing energy consumption and emissions, increasing road safety, and improving traffic flow. The Grand Cooperative Driving Challenge (GCDC) 2016 was an implementation oriented project with the aim to accelerate research and development in the field. This paper describes the development of the two vehicle systems with which KTH participated in GCDC 2016. It presents a reference system architecture for collaborative automated driving as well as its instantiation on two conceptually different vehicles: a Scania truck and the research concept vehicle, built at KTH. We describe the common system architecture, as well as the implementation of a selection of shared and individual system functionalities, such as V2X communication, localization, state estimation, and longitudinal and lateral control. We also present a novel approach to trajectory tracking control for a four-wheel steering vehicle using model predictive control and a novel method for achieving fair data age distribution in vehicular communications.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Automated vehicles, cooperative automated driving, cyber-physical system architecture, intelligent transportation systems, model predictive control, vehicular communication
National Category
Vehicle Engineering
Identifiers
urn:nbn:se:kth:diva-226785 (URN)10.1109/TITS.2017.2684623 (DOI)000429017300025 ()2-s2.0-85017136485 (Scopus ID)
Funder
Swedish Transport Administration
Note

QC 20180502

Available from: 2018-05-02 Created: 2018-05-02 Last updated: 2018-05-02Bibliographically approved
Lima, P. F., Oliveira, R., Mårtensson, J. & Wahlberg, B. (2017). Minimizing Long Vehicles Overhang Exceeding the Drivable Surface via Convex Path Optimization. In: : . Paper presented at IEEE Intelligent Transportation Systems Conference. IEEE
Open this publication in new window or tab >>Minimizing Long Vehicles Overhang Exceeding the Drivable Surface via Convex Path Optimization
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel path planning algo- rithm for on-road autonomous driving. The algorithm targets long and wide vehicles, in which the overhangs (i.e., the vehicle chassis extending beyond the front and rear wheelbase) can endanger other vehicles, pedestrians, or even the vehicle itself. The vehicle motion is described in a road-aligned coordinate frame. A novel method for computing the vehicle limits is proposed guaranteeing feasibility of the planned path when con- verted back into the original coordinate frame. The algorithm is posed as a convex optimization that takes into account the exact dimensions of the vehicle and the road, while minimizing the amount of overhang outside of the drivable surface.

The results of the proposed algorithm are compared in a simulation of a real road scenario against a centerline tracking scheme. The results show a significant decrease on the amount of overhang area outside of the drivable surface, leading to an increased safety in driving maneuvers. The real-time applicability of the method is shown, by using it in a receding- horizon framework. 

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Path planning, Optimization, Autonomous Driving
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-220575 (URN)
Conference
IEEE Intelligent Transportation Systems Conference
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-03-27Bibliographically approved
Lima, P. F., Oliveira, R., Mårtensson, J. & Wahlberg, B. (2017). Minimizing Long Vehicles Overhang Exceeding the Drivable Surface via Convex Path Optimization. In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at 20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN. IEEE
Open this publication in new window or tab >>Minimizing Long Vehicles Overhang Exceeding the Drivable Surface via Convex Path Optimization
2017 (English)In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a novel path planning algorithm for on-road autonomous driving. The algorithm targets long and wide vehicles, in which the overhangs (i.e., the vehicle chassis extending beyond the front and rear wheelbase) can endanger other vehicles, pedestrians, or even the vehicle itself. The vehicle motion is described in a road-aligned coordinate frame. A novel method for computing the vehicle limits is proposed guaranteeing feasibility of the planned path when converted back into the original coordinate frame. The algorithm is posed as a convex optimization that takes into account the exact dimensions of the vehicle and the road, while minimizing the amount of overhang outside of the drivable surface. The results of the proposed algorithm are compared in a simulation of a real road scenario against a centerline tracking scheme. The results show a significant decrease on the amount of overhang area outside of the drivable surface, leading to an increased safety in driving maneuvers. The real-time applicability of the method is shown, by using it in a recedinghorizon framework.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-230878 (URN)10.1109/ITSC.2017.8317754 (DOI)000432373000161 ()2-s2.0-85046256908 (Scopus ID)978-1-5386-1526-3 (ISBN)
Conference
20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN
Note

QC 20180618

Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved
Henriksson, M., Flärdh, O. & Mårtensson, J. (2017). Optimal Powertrain Control of a Heavy-Duty Vehicle Under Varying Speed Requirements. In: : . Paper presented at 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). IEEE
Open this publication in new window or tab >>Optimal Powertrain Control of a Heavy-Duty Vehicle Under Varying Speed Requirements
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Reducing the fuel consumption is a major issue in the vehicle industry. In this paper, it is done by formulatinga driving mission of a heavy-duty truck as an optimal control problem and solving it using dynamic programming.The vehicle model includes an engine and a gearbox with parameters based on measurements in test cells. The dynamic programming algorithm is solved by considering four specifictypes of transitions: transitions between the same gear, coastingin neutral gear, coasting with a gear engaged with no fuel injection and transitions involving gear changes. Simulations are performed on a driving cycle commonly used for testing distribution type of driving. In order to make sure that the truck does not deviate too much from a normal way of driving, restrictions on maximum and minimum allowed velocities are imposed based on statistics from real traffic data. The simulations show that 12.7% fuel can be saved without increasing the trip time by allowing the truck to engage neutral gear and make small deviations from the reference trajectory.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Optimal control, heavy-duty vehicles, powertrain
National Category
Control Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-220134 (URN)10.1109/ITSC.2017.8317615 (DOI)000432373000033 ()2-s2.0-85046298697 (Scopus ID)
Conference
2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)
Funder
VINNOVA, 2015-02325
Note

QC 20180117

Available from: 2017-12-15 Created: 2017-12-15 Last updated: 2018-06-18Bibliographically approved
Lima, P. F., Nilsson, M., Trincavelli, M., Mårtensson, J. & Wahlberg, B. (2017). Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck. IEEE Transactions on Intelligent Vehicles, 2(4), 238-250
Open this publication in new window or tab >>Spatial Model Predictive Control for Smooth and Accurate Steering of an Autonomous Truck
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2017 (English)In: IEEE Transactions on Intelligent Vehicles, ISSN 2379-8858, Vol. 2, no 4, p. 238-250Article in journal (Refereed) Published
Abstract [en]

In this paper, we present an algorithm for lateral control of a vehicle – a smooth and accurate model predictive controller. The fundamental difference compared to a standard MPC is that the driving smoothness is directly addressed in the cost function. The controller objective is based on the minimization of the first- and second-order spatial derivatives of the curvature. By doing so, jerky commands to the steering wheel, which could lead to permanent damage on the steering components and vehicle structure, are avoided. A good path tracking accuracy is ensured by adding constraints to avoid deviations from the reference path. Finally, the controller is experimentally tested and evaluated on a Scania construction truck. The evaluation is performed at Scania’s facilities near So ̈derta ̈lje, Sweden via two different paths: a precision track that resembles a mining scenario and a high-speed test track that resembles a highway situation. Even using a linearized kinematic vehicle to predict the vehicle motion, the performance of the proposed controller is encouraging, since the deviation from the path never exceeds 30 cm. It clearly outperforms an industrial pure-pursuit controller in terms of path accuracy and a standard MPC in terms of driving smoothness. 

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Autonomous vehicles, predictive control
National Category
Control Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kth:diva-220573 (URN)10.1109/TIV.2017.2767279 (DOI)
Projects
iQMatic
Funder
VINNOVA, 2012-04626
Note

QC 20180117

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-01-17Bibliographically approved
Graf Plessen, M., Lima, P. F., Mårtensson, J., Bemporad, A. & Wahlberg, B. (2017). Spatial-based Trajectory Planning under Vehicle Dimension Constraints Using Sequential Linear Programming. In: : . Paper presented at IEEE Intelligent Transportation Systems Conference. IEEE
Open this publication in new window or tab >>Spatial-based Trajectory Planning under Vehicle Dimension Constraints Using Sequential Linear Programming
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2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, ob- stacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are lin- earized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space. 

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Path Planning, Optimization, Autonomous driving
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-220574 (URN)
Conference
IEEE Intelligent Transportation Systems Conference
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-01-19Bibliographically approved
Lima, P. F., Mårtensson, J. & Wahlberg, B. (2017). Stability Conditions for Linear Time-Varying Model Predictive Control in Autonomous Driving. In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017: . Paper presented at 56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne Convention and Exhibition Centre (MCEC)Melbourne, Australia, 12 December 2017 through 15 December 2017 (pp. 2775-2782). IEEE
Open this publication in new window or tab >>Stability Conditions for Linear Time-Varying Model Predictive Control in Autonomous Driving
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, IEEE, 2017, p. 2775-2782Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents stability conditions when designing a linear time-varying model predictive controller for lateral control of an autonomous vehicle. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost. We detail how to compute the terminal state and the terminal cost for the linear time-varying case, and interpret the obtained results in the light of an autonomous driving application. To determine the stability conditions, the concept of multi-model description is used, where the linear time-varying model is separated into a finite number of time- invariant models that depend on a single parameter. The terminal set is the maximum positive invariant set of the multi- model description and the terminal cost is the result of a min-max optimization that determines the worst time-invariant model if used as a prediction model. In fact, in the autonomous driving case, we show that the min-max approach is a convex optimization problem. The stability conditions are computed offline, maintain the convexity of the optimization, and do not affect the execution time of the controller. In simulation, we demonstrate the stabilizing effectiveness of the proposed conditions through an illustrative example of path following with a heavy-duty vehicle. 

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords
Autonomous Driving, Model Predictive Control, Stability
National Category
Control Engineering
Identifiers
urn:nbn:se:kth:diva-220576 (URN)10.1109/CDC.2017.8264062 (DOI)000424696902110 ()2-s2.0-85046277035 (Scopus ID)978-1-5090-2873-3 (ISBN)
Conference
56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne Convention and Exhibition Centre (MCEC)Melbourne, Australia, 12 December 2017 through 15 December 2017
Funder
VINNOVA
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-06-01Bibliographically approved
Plessen, M. G., Lima, P. F., Mårtensson, J., Bemporad, A. & Wahlberg, B. (2017). Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming. In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at 20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN. IEEE
Open this publication in new window or tab >>Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming
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2017 (English)In: 2017 IEEE 20TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2017Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained environments. A sequential linear programming (SLP) algorithm is motivated. A linear program (LP) is solved at each SLP-iteration. The relation between LP formulation and maximum admissible traveling speeds within vehicle tire friction limits is discussed. The proposed method is evaluated in a roomy and in a tight maneuvering driving scenario, whereby a comparison to a semi-analytical clothoid-based path planner is given. Effectiveness is demonstrated particularly for very constrained environments, requiring to account for constraints and planning over the entire obstacle constellation space.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-230879 (URN)10.1109/ITSC.2017.8317665 (DOI)000432373000077 ()2-s2.0-85046257090 (Scopus ID)978-1-5386-1526-3 (ISBN)
Conference
20th IEEE International Conference on Intelligent Transportation Systems (ITSC), OCT 16-19, 2017, Yokohama, JAPAN
Note

QC 20180618

Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18Bibliographically approved
Besselink, B., Turri, V., Van De Hoef, S. H., Liang, K.-Y., Alam, A., Mårtensson, J. & Johansson, K. H. (2016). Cyber-Physical Control of Road Freight Transport. Proceedings of the IEEE, 104(5), 1128-1141, Article ID 7437386.
Open this publication in new window or tab >>Cyber-Physical Control of Road Freight Transport
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2016 (English)In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 104, no 5, p. 1128-1141, article id 7437386Article in journal (Refereed) Published
Abstract [en]

Freight transportation is of outmost importance in our society and is continuously increasing. At the same time, transporting goods on roads accounts for about 26% of the total energy consumption and 18% of all greenhouse gas emissions in the European Union. Despite the influence the transportation system has on our energy consumption and the environment, road transportation is mainly done by individual long-haulage trucks with no real-time coordination or global optimization. In this paper, we review how modern information and communication technology supports a cyber-physical transportation system architecture with an integrated logistic system coordinating fleets of trucks traveling together in vehicle platoons. From the reduced air drag, platooning trucks traveling close together can save about 10% of their fuel consumption. Utilizing road grade information and vehicle-to-vehicle communication, a safe and fuel-optimized cooperative look-ahead control strategy is implemented on top of the existing cruise controller. By optimizing the interaction between vehicles and platoons of vehicles, it is shown that significant improvements can be achieved. An integrated transport planning and vehicle routing in the fleet management system allows both small and large fleet owners to benefit from the collaboration. A realistic case study with 200 heavy-duty vehicles performing transportation tasks in Sweden is described. Simulations show overall fuel savings at more than 5% thanks to coordinated platoon planning. It is also illustrated how well the proposed cooperative look-ahead controller for heavy-duty vehicle platoons manages to optimize the velocity profiles of the vehicles over a hilly segment of the considered road network.

Place, publisher, year, edition, pages
IEEE, 2016
Keywords
automated highways, Automotive engineering, intelligent transportation systems, intelligent vehicles, networked control systems, vehicular communication, Automobiles, Controllers, Cooperative communication, Cruise control, Energy utilization, Fleet operations, Fuel economy, Fuels, Gas emissions, Global optimization, Greenhouse gases, Motor transportation, Numerical control systems, Roads and streets, Traffic control, Transportation, Trucks, Vehicle to vehicle communications, Vehicles, Fleet management system, Information and Communication Technologies, Integrated transport, Physical transportation, Real time coordination, Road freight transport, Total energy consumption, Transportation system, Freight transportation
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-186985 (URN)10.1109/JPROC.2015.2511446 (DOI)000374864600019 ()2-s2.0-84961572731 (Scopus ID)
Funder
Swedish Research CouncilEU, FP7, Seventh Framework ProgrammeVINNOVAKnut and Alice Wallenberg Foundation
Note

QC 20160516

Available from: 2016-05-16 Created: 2016-05-16 Last updated: 2017-11-30Bibliographically approved
Lima, P. F., Nilsson, M., Trincavelli, M., Mårtensson, J. & Wahlberg, B. (2016). Experimental evaluation of economic model predictive control for an autonomous truck. In: : . Paper presented at IEEE Intelligent Vehicles Symposium (IV)IV 2016; Gotenburg Sweden, 19 June 2016 through 22 June 2016. IEEE
Open this publication in new window or tab >>Experimental evaluation of economic model predictive control for an autonomous truck
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2016 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a controller for smooth autonomous path following. The controller is formulated as an economic model predictive controller. The economic cost introduced in the objective function leads to a smooth driving, since we minimize the first and second derivatives of the curvature function (i.e., we encourage linear curvature profiles). Since the curvature in clothoids varies linearly with the path arc-length, we use the smoothness and comfort characteristics of clothoid-driving to obtain a compact and intuitive controller formulation. We enforce convergence of the controller to the reference path with soft constraints that avoid deviations from the reference path. Finally, we present real life experiments where the controller is deployed on a Scania construction truck that show that the proposed controller outperforms a pure-pursuit controller. Moreover, we detail how the few tuning parameters can affect the obtained solution in practice.

Place, publisher, year, edition, pages
IEEE, 2016
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:kth:diva-200340 (URN)000390845600113 ()2-s2.0-84983341615 (Scopus ID)
Conference
IEEE Intelligent Vehicles Symposium (IV)IV 2016; Gotenburg Sweden, 19 June 2016 through 22 June 2016
Note

QC 20170330

Available from: 2017-01-24 Created: 2017-01-24 Last updated: 2017-06-16Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3672-5316

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