Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways
Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Han, Xu [verfasserIn] Xu, Runsheng [verfasserIn] Xia, Xin [verfasserIn] Sathyan, Anoop [verfasserIn] Guo, Yi [verfasserIn] Bujanović, Pavle [verfasserIn] Leslie, Ed [verfasserIn] Goli, Mohammad [verfasserIn] Ma, Jiaqi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Connected and automated vehicle (CAV) Cooperative driving automation |
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| Übergeordnetes Werk: |
Enthalten in: Transportation research / C - Amsterdam [u.a.] : Elsevier Science, 1993, 145 |
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| Übergeordnetes Werk: |
volume:145 |
| DOI / URN: |
10.1016/j.trc.2022.103952 |
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| Katalog-ID: |
ELV008879982 |
|---|
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| 245 | 1 | 0 | |a Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways |
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| 520 | |a Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. | ||
| 650 | 4 | |a Connected and automated vehicle (CAV) | |
| 650 | 4 | |a Cooperative driving automation | |
| 650 | 4 | |a Cooperative driving | |
| 650 | 4 | |a Automated driving system (ADS) | |
| 650 | 4 | |a Platooning | |
| 650 | 4 | |a Finite state machine (FSM) | |
| 650 | 4 | |a Genetic fuzzy system (GFS) | |
| 700 | 1 | |a Xu, Runsheng |e verfasserin |4 aut | |
| 700 | 1 | |a Xia, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Sathyan, Anoop |e verfasserin |0 (orcid)0000-0003-2414-9515 |4 aut | |
| 700 | 1 | |a Guo, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Bujanović, Pavle |e verfasserin |4 aut | |
| 700 | 1 | |a Leslie, Ed |e verfasserin |4 aut | |
| 700 | 1 | |a Goli, Mohammad |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Jiaqi |e verfasserin |4 aut | |
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10.1016/j.trc.2022.103952 doi (DE-627)ELV008879982 (ELSEVIER)S0968-090X(22)00365-5 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl Han, Xu verfasserin aut Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. Connected and automated vehicle (CAV) Cooperative driving automation Cooperative driving Automated driving system (ADS) Platooning Finite state machine (FSM) Genetic fuzzy system (GFS) Xu, Runsheng verfasserin aut Xia, Xin verfasserin aut Sathyan, Anoop verfasserin (orcid)0000-0003-2414-9515 aut Guo, Yi verfasserin aut Bujanović, Pavle verfasserin aut Leslie, Ed verfasserin aut Goli, Mohammad verfasserin aut Ma, Jiaqi verfasserin aut Enthalten in Transportation research / C Amsterdam [u.a.] : Elsevier Science, 1993 145 Online-Ressource (DE-627)320532070 (DE-600)2015891-9 (DE-576)259484962 1879-2359 nnns volume:145 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr AR 145 |
| spelling |
10.1016/j.trc.2022.103952 doi (DE-627)ELV008879982 (ELSEVIER)S0968-090X(22)00365-5 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl Han, Xu verfasserin aut Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. Connected and automated vehicle (CAV) Cooperative driving automation Cooperative driving Automated driving system (ADS) Platooning Finite state machine (FSM) Genetic fuzzy system (GFS) Xu, Runsheng verfasserin aut Xia, Xin verfasserin aut Sathyan, Anoop verfasserin (orcid)0000-0003-2414-9515 aut Guo, Yi verfasserin aut Bujanović, Pavle verfasserin aut Leslie, Ed verfasserin aut Goli, Mohammad verfasserin aut Ma, Jiaqi verfasserin aut Enthalten in Transportation research / C Amsterdam [u.a.] : Elsevier Science, 1993 145 Online-Ressource (DE-627)320532070 (DE-600)2015891-9 (DE-576)259484962 1879-2359 nnns volume:145 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr AR 145 |
| allfields_unstemmed |
10.1016/j.trc.2022.103952 doi (DE-627)ELV008879982 (ELSEVIER)S0968-090X(22)00365-5 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl Han, Xu verfasserin aut Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. Connected and automated vehicle (CAV) Cooperative driving automation Cooperative driving Automated driving system (ADS) Platooning Finite state machine (FSM) Genetic fuzzy system (GFS) Xu, Runsheng verfasserin aut Xia, Xin verfasserin aut Sathyan, Anoop verfasserin (orcid)0000-0003-2414-9515 aut Guo, Yi verfasserin aut Bujanović, Pavle verfasserin aut Leslie, Ed verfasserin aut Goli, Mohammad verfasserin aut Ma, Jiaqi verfasserin aut Enthalten in Transportation research / C Amsterdam [u.a.] : Elsevier Science, 1993 145 Online-Ressource (DE-627)320532070 (DE-600)2015891-9 (DE-576)259484962 1879-2359 nnns volume:145 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr AR 145 |
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10.1016/j.trc.2022.103952 doi (DE-627)ELV008879982 (ELSEVIER)S0968-090X(22)00365-5 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl Han, Xu verfasserin aut Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. Connected and automated vehicle (CAV) Cooperative driving automation Cooperative driving Automated driving system (ADS) Platooning Finite state machine (FSM) Genetic fuzzy system (GFS) Xu, Runsheng verfasserin aut Xia, Xin verfasserin aut Sathyan, Anoop verfasserin (orcid)0000-0003-2414-9515 aut Guo, Yi verfasserin aut Bujanović, Pavle verfasserin aut Leslie, Ed verfasserin aut Goli, Mohammad verfasserin aut Ma, Jiaqi verfasserin aut Enthalten in Transportation research / C Amsterdam [u.a.] : Elsevier Science, 1993 145 Online-Ressource (DE-627)320532070 (DE-600)2015891-9 (DE-576)259484962 1879-2359 nnns volume:145 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr AR 145 |
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10.1016/j.trc.2022.103952 doi (DE-627)ELV008879982 (ELSEVIER)S0968-090X(22)00365-5 DE-627 ger DE-627 rda eng 380 DE-600 55.84 bkl Han, Xu verfasserin aut Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. Connected and automated vehicle (CAV) Cooperative driving automation Cooperative driving Automated driving system (ADS) Platooning Finite state machine (FSM) Genetic fuzzy system (GFS) Xu, Runsheng verfasserin aut Xia, Xin verfasserin aut Sathyan, Anoop verfasserin (orcid)0000-0003-2414-9515 aut Guo, Yi verfasserin aut Bujanović, Pavle verfasserin aut Leslie, Ed verfasserin aut Goli, Mohammad verfasserin aut Ma, Jiaqi verfasserin aut Enthalten in Transportation research / C Amsterdam [u.a.] : Elsevier Science, 1993 145 Online-Ressource (DE-627)320532070 (DE-600)2015891-9 (DE-576)259484962 1879-2359 nnns volume:145 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_165 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 55.84 Straßenverkehr AR 145 |
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Han, Xu @@aut@@ Xu, Runsheng @@aut@@ Xia, Xin @@aut@@ Sathyan, Anoop @@aut@@ Guo, Yi @@aut@@ Bujanović, Pavle @@aut@@ Leslie, Ed @@aut@@ Goli, Mohammad @@aut@@ Ma, Jiaqi @@aut@@ |
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Han, Xu |
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Han, Xu ddc 380 bkl 55.84 misc Connected and automated vehicle (CAV) misc Cooperative driving automation misc Cooperative driving misc Automated driving system (ADS) misc Platooning misc Finite state machine (FSM) misc Genetic fuzzy system (GFS) Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways |
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380 DE-600 55.84 bkl Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways Connected and automated vehicle (CAV) Cooperative driving automation Cooperative driving Automated driving system (ADS) Platooning Finite state machine (FSM) Genetic fuzzy system (GFS) |
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strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways |
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Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways |
| abstract |
Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. |
| abstractGer |
Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. |
| abstract_unstemmed |
Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals. |
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| score |
7.3991594 |