Leveraging Human Driving Preferences to Predict Vehicle Speed
Accurate speed prediction is practically critical to eco-safe driving for intelligent vehicles. Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage h...
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| Veröffentlicht in: | IEEE transactions on intelligent transportation systems 2022-08, Vol.23 (8), p.11137-11147 |
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| container_title | IEEE transactions on intelligent transportation systems |
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| creator | Yang, Sen Wang, Wenshuo Xi, Junqiang |
| description | Accurate speed prediction is practically critical to eco-safe driving for intelligent vehicles. Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver's driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. Experimental results demonstrate that our proposed Oriented-HSMM method reaches the best results and achieves a satisfying performance with a low mean absolute error (4.16 km/h) and root mean square error (5.08 km/h) at a 200 m prediction horizon. |
| doi_str_mv | 10.1109/TITS.2021.3101000 |
| format | Article |
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Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver's driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. Experimental results demonstrate that our proposed Oriented-HSMM method reaches the best results and achieves a satisfying performance with a low mean absolute error (4.16 km/h) and root mean square error (5.08 km/h) at a 200 m prediction horizon.</description><identifier>ISSN: 1524-9050</identifier><identifier>EISSN: 1558-0016</identifier><identifier>DOI: 10.1109/TITS.2021.3101000</identifier><identifier>CODEN: ITISFG</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Driving ; driving preferences ; Hidden Markov models ; hidden semi-Markov model ; Intelligent vehicles ; Markov chains ; Prediction algorithms ; Random variables ; Roads ; Traffic speed ; Trajectory ; Vehicle dynamics ; Vehicle speed prediction ; Vehicles</subject><ispartof>IEEE transactions on intelligent transportation systems, 2022-08, Vol.23 (8), p.11137-11147</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver's driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. Experimental results demonstrate that our proposed Oriented-HSMM method reaches the best results and achieves a satisfying performance with a low mean absolute error (4.16 km/h) and root mean square error (5.08 km/h) at a 200 m prediction horizon.</description><subject>Algorithms</subject><subject>Driving</subject><subject>driving preferences</subject><subject>Hidden Markov models</subject><subject>hidden semi-Markov model</subject><subject>Intelligent vehicles</subject><subject>Markov chains</subject><subject>Prediction algorithms</subject><subject>Random variables</subject><subject>Roads</subject><subject>Traffic speed</subject><subject>Trajectory</subject><subject>Vehicle dynamics</subject><subject>Vehicle speed prediction</subject><subject>Vehicles</subject><issn>1524-9050</issn><issn>1558-0016</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kE9LAzEQxYMoWKsfQLwseN46s9nsJgcPUv-0UFBo9RrSZLZuaXdrsi347c3S4mnmDe_NDD_GbhFGiKAeFtPFfJRBhiOOgABwxgYohEwBsDjv-yxPFQi4ZFchrOM0F4gD9jijA3mzqptVMtlvTZM8-_rQqw9PFXlqLIWka3vpatslX_Rd2w0l8x2Ru2YXldkEujnVIft8fVmMJ-ns_W06fpqlNlO8S62i0rq8Eg6UAVVwJYoMqMrLwpmcuDTLonSFdKW1lSUhootDZchJJ5Yc-ZDdH_fufPuzp9Dpdbv3TTypsxIgRyGVjC48uqxvQ4jv652vt8b_agTdU9I9Jd1T0idKMXN3zNRE9O9XkQ1Hyf8AE_hiqw</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Yang, Sen</creator><creator>Wang, Wenshuo</creator><creator>Xi, Junqiang</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver's driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. 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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithms Driving driving preferences Hidden Markov models hidden semi-Markov model Intelligent vehicles Markov chains Prediction algorithms Random variables Roads Traffic speed Trajectory Vehicle dynamics Vehicle speed prediction Vehicles |
| title | Leveraging Human Driving Preferences to Predict Vehicle Speed |
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