Scenario Model Predictive Control for Lane Change Assistance and Autonomous Driving on Highways
This paper presents a novel design of control algorithms for lane change assistance and autonomous driving on highways, based on recent results in Scenario Model Predictive Control (SCMPC). The basic idea is to account for the uncertainty in the traffic environment by a small number of future scenar...
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| Veröffentlicht in: | IEEE intelligent transportation systems magazine 2017-01, Vol.9 (3), p.23-35 |
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| container_title | IEEE intelligent transportation systems magazine |
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| creator | Cesari, Gianluca Schildbach, Georg Carvalho, Ashwin Borrelli, Francesco |
| description | This paper presents a novel design of control algorithms for lane change assistance and autonomous driving on highways, based on recent results in Scenario Model Predictive Control (SCMPC). The basic idea is to account for the uncertainty in the traffic environment by a small number of future scenarios, which is intuitive and computationally efficient. These scenarios can be generated by any model-based or data-based approach. The paper discusses the SCMPC design procedure, which is simple and can be generalized to other control challenges in automated driving, as well as the controller's robustness properties. Experimental results demonstrate the effectiveness of the SCMPC algorithm and its performance in lane change situations on highways. |
| doi_str_mv | 10.1109/MITS.2017.2709782 |
| format | Article |
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Experimental results demonstrate the effectiveness of the SCMPC algorithm and its performance in lane change situations on highways.</description><subject>Automatic control</subject><subject>Control algorithms</subject><subject>Driving</subject><subject>Hidden Markov models</subject><subject>Highways</subject><subject>Mathematical models</subject><subject>Predictive control</subject><subject>Predictive models</subject><subject>Robustness</subject><subject>Stochastic processes</subject><subject>Trajectory</subject><subject>Vehicles</subject><issn>1939-1390</issn><issn>1941-1197</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kFtrAjEQhUNpoWL9AaUvgT6vzWXdbB7FXhSUFrTPISazGtHEJrsW_313UTovMwznzGE-hB4pGVJK5MtitloOGaFiyASRomQ3qEdlTjNKpbjtZi4zyiW5R4OUdqQtzsqCyR5SSwNeRxfwIljY468I1pnanQBPgq9j2OMqRDzXvl1std8AHqfkUq29Aay9xeOmDj4cQpPwa3Qn5zc4eDx1m-2vPqcHdFfpfYLBtffR9_vbajLN5p8fs8l4nhkmeZ1RUzKhgRso8kKISvKq4LIUueVrQWje_mVBM2IYt2ytrSgJK8wIgGsQjFjeR8-Xu8cYfhpItdqFJvo2UlHJuOAFG5FWRS8qE0NKESp1jO6g41lRojqUqkOpOpTqirL1PF08DgD-9UJKUuSC_wExJm9J</recordid><startdate>20170101</startdate><enddate>20170101</enddate><creator>Cesari, Gianluca</creator><creator>Schildbach, Georg</creator><creator>Carvalho, Ashwin</creator><creator>Borrelli, Francesco</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| ispartof | IEEE intelligent transportation systems magazine, 2017-01, Vol.9 (3), p.23-35 |
| issn | 1939-1390 1941-1197 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Automatic control Control algorithms Driving Hidden Markov models Highways Mathematical models Predictive control Predictive models Robustness Stochastic processes Trajectory Vehicles |
| title | Scenario Model Predictive Control for Lane Change Assistance and Autonomous Driving on Highways |
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