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Original Articles

Connected and automated road vehicles: state of the art and future challenges

ORCID Icon, , ORCID Icon, , , ORCID Icon & ORCID Icon show all
Pages 672-704 | Received 27 Aug 2019, Accepted 03 Mar 2020, Published online: 29 Mar 2020

References

  • Taxonomy and definitions for terms related to on-road motor vehicle automated driving systems. SAE International; 2016. (SAE J3016_201609).
  • Dedicated short range communications (DSRC) message set dictionary set. SAE International; 2016. (SAE J2735SET_201603).
  • 3GPP release 15. 3rd Generation partnership project; 2018. Available from: https://www.3gpp.org/release-15
  • Campbell M, Egerstedt M, How J, et al. Autonomous driving in urban environments: approaches, lessons and challenges. Philos Trans R Soc A. 2010;368(1928):4649–4672. doi: 10.1098/rsta.2010.0110
  • Van Brummelen J, O'Brien M, Gruyer D, et al. Autonomous vehicle perception: the technology of today and tomorrow. Transp Res Part C. 2018;89:384–406. doi: 10.1016/j.trc.2018.02.012
  • Orosz G, Ge JI, He CR, et al. Seeing beyond the line of sight – controlling connected automated vehicles. ASME Mech Eng Mag. 2017;139(12):S8–S12. doi: 10.1115/1.2017-Dec-8
  • Popp K, Schiehlen W. Ground vehicle dynamics. Berlin: Springer; 2010.
  • Schramm D, Hiller M, Bardini R. Ground vehicle dynamics. Berlin: Springer; 2014.
  • Rajamani R. Vehicle dynamics and control. New York (NY): Springer; 2012.
  • Ulsoy AG, Peng H, Cakmakci M. Automotive control systems. Cambridge: Cambridge University Press; 2012.
  • Bloch A. Nonholonomic mechanics and control. New York (NY): Springer; 2003.
  • Sapio VD. Advanced analytical dynamics: theory and applications. Cambridge: Cambridge University Press; 2017.
  • Várszegi B, Takács D, Orosz G. On the nonlinear dynamics of automated vehicles – a nonholonomic approach. Eur J Mech A. 2019;74:371–380. doi: 10.1016/j.euromechsol.2018.11.015
  • Oda K, Takeuchi H, Tsujii M, et al. Practical estimator for self-tuning automotive cruise control. 1991 American Control Conference; Boston (MA). IEEE; 1991. p. 2066–2071.
  • Vahidi A, Stefanopoulou A, Peng H. Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Veh Syst Dyn. 2005;43(1):31–55. doi: 10.1080/00423110412331290446
  • Fathy HK, Kang D, Stein JL. Online vehicle mass estimation using recursive least squares and supervisory data extraction. 2008 American Control Conference; Seattle (WA). IEEE; 2008. p. 1842–1848.
  • Ploeg J, Shukla DPvan de Wouw N, et al. Controller synthesis for string stability of vehicle platoons. IEEE Trans Intell Transp Syst. 2014;15(2):854–865. doi: 10.1109/TITS.2013.2291493
  • Nilsson P, Hussien O, Balkan A. Correct-by-construction adaptive cruise control: two approaches. IEEE Trans Control Syst Technol. 2016;24(4):1294–1307. doi: 10.1109/TCST.2015.2501351
  • Xu S, Peng H. Design, analysis, and experiments of preview path tracking control for autonomous vehicles. IEEE Trans Intell Transp Syst. 2020;21(1):48–58. doi: 10.1109/TITS.2019.2892926
  • Guldner J, Tan HS, Patwardhan S. Analysis of automatic steering control for highway vehicles with look-down lateral reference systems. Veh Syst Dyn. 1996;26(4):243–269. doi: 10.1080/00423119608969311
  • Rossetter EJ, Gerdes JC. Lyapunov based performance guarantees for the potential field lane-keeping assistance system. J Dyn Syst Meas Control. 2006;128(3):510–522. doi: 10.1115/1.2192835
  • Talvala KL, Gerdes JC. Lanekeeping at the limits of handling: stability via lyapunov functions and a comparison with stability control. ASME 2008 Dynamic Systems and Control Conference; Ann Arbor (MI). American Society of Mechanical Engineers; 2008. p. 361–368.
  • Son YS, Kim W, Lee SH, et al. Robust multirate control scheme with predictive virtual lanes for lane-keeping system of autonomous highway driving. IEEE Trans Veh Technol. 2014;64(8):3378–3391. doi: 10.1109/TVT.2014.2356204
  • Dai S, Koutsoukos X. Safety analysis of automotive control systems using multi-modal port-hamiltonian systems. 19th International Conference on Hybrid Systems: Computation and Control; Vienna, Austria. ACM; 2016. p. 105–114.
  • Xu X, Grizzle JW, Tabuada P, et al. Correctness guarantees for the composition of lane keeping and adaptive cruise control. IEEE Trans Autom Sci Eng. 2017;15(3):1216–1229. doi: 10.1109/TASE.2017.2760863
  • González D, Pérez J, Milanés V, et al. A review of motion planning techniques for automated vehicles. IEEE Trans Intell Transp Syst. 2015;17(4):1135–1145. doi: 10.1109/TITS.2015.2498841
  • Paden B, Čáp M, Yong SZ, et al. A survey of motion planning and control techniques for self-driving urban vehicles. IEEE Trans Intel Veh. 2016;1(1):33–55. doi: 10.1109/TIV.2016.2578706
  • Kuwata Y, Teo J, Fiore G, et al. Real-time motion planning with applications to autonomous urban driving. IEEE Trans Control Syst Technol. 2009;17(5):1105–1118. doi: 10.1109/TCST.2008.2012116
  • Karaman S, Walter MR, Perez A, et al. Anytime motion planning using the RRT. 2011 IEEE International Conference on Robotics and Automation; Shanghai, China. IEEE; 2011. p. 1478–1483.
  • Howard TM, Green CJ, Kelly A, et al. State space sampling of feasible motions for high-performance mobile robot navigation in complex environments. J Field Rob. 2008;25(6–7):325–345. doi: 10.1002/rob.20244
  • McNaughton M, Urmson C, Dolan JM, et al. Motion planning for autonomous driving with a conformal spatiotemporal lattice. IEEE International Conference on Robotics and Automation; Shanghai, China. IEEE; 2011. p. 4889–4895.
  • Gu T, Snider J, Dolan JM, et al. Focused trajectory planning for autonomous on-road driving. IEEE Intelligent Vehicles Symposium (IV); Gold Coast, Australia. IEEE; 2013. p. 547–552.
  • Berntorp K, Weiss A, Danielson C, et al. Automated driving: safe motion planning using positively invariant sets. 20th IEEE International Conference on Intelligent Transportation Systems; Yokohama, Japan. IEEE; 2017. p. 1–6.
  • Kogan D, Murray RM. Optimization-based navigation for the DARPA grand challenge. 45th IEEE Conference on Decision and Control; San Diego (CA). IEEE; 2006.
  • Gu T, Dolan JM. On-road motion planning for autonomous vehicles. International Conference on Intelligent Robotics and Applications; Montreal, Canada. Springer; 2012. p. 588–597.
  • Shia VA, Gao Y, Vasudevan R, et al. Semiautonomous vehicular control using driver modeling. IEEE Trans Intell Transp Syst. 2014;15(6):2696–2709. doi: 10.1109/TITS.2014.2325776
  • Liu J, Jayakumar P, Stein JL, et al. Combined speed and steering control in high speed autonomous ground vehicles for obstacle avoidance using model predictive control. IEEE Trans Veh Technol. 2017;66(10):8746–8763. doi: 10.1109/TVT.2017.2707076
  • Febbo H, Liu J, Jayakumar P, et al. Moving obstacle avoidance for large, high-speed autonomous ground vehicles. American Control Conference; Seattle (WA). IEEE; 2017. p. 5568–5573.
  • Wurts J, Stein JL, Ersal T. Minimum slip collision imminent steering in curved roads using nonlinear model predictive control. American Control Conference; Philadelphia (PA). IEEE; 2019. p. 3975–3980.
  • Gao Y, Lin T, Borrelli F, et al. Predictive control of autonomous ground vehicles with obstacle avoidance on slippery roads. ASME 2010 Dynamic Systems and Control Conference; Cambridge (MA). American Society of Mechanical Engineers; 2010. p. 265–272.
  • Falcone P, Borrelli F, Asgari J, et al. Predictive active steering control for autonomous vehicle systems. IEEE Trans Control Syst Technol. 2007;15(3):566–580. doi: 10.1109/TCST.2007.894653
  • Falcone P, Eric Tseng H, Borrelli F, et al. MPC-based yaw and lateral stabilisation via active front steering and braking. Veh Syst Dyn. 2008;46(S1):611–628. doi: 10.1080/00423110802018297
  • Beal CE, Gerdes JC. Model predictive control for vehicle stabilization at the limits of handling. IEEE Trans Control Syst Technol. 2012;21(4):1258–1269. doi: 10.1109/TCST.2012.2200826
  • Althoff M, Dolan JM. Online verification of automated road vehicles using reachability analysis. IEEE Trans Rob. 2014;30(4):903–918. doi: 10.1109/TRO.2014.2312453
  • Pek C, Zahn P, Althoff M. Verifying the safety of lane change maneuvers of self-driving vehicles based on formalized traffic rules. 2017 IEEE Intelligent Vehicles Symposium (IV); Los Angeles (CA). IEEE; 2017. p. 1477–1483.
  • Liniger A, Lygeros J. Real-time control for autonomous racing based on viability theory. IEEE Trans Control Syst Technol. 2017;27(2):464–478. doi: 10.1109/TCST.2017.2772903
  • Ames AD, Grizzle JW, Tabuada P. Control barrier function based quadratic programs with application to adaptive cruise control. 53rd IEEE Conference on Decision and Control; Los Angeles (CA). IEEE; 2014. p. 6271–6278.
  • Ames AD, Xu X, Grizzle JW, et al. Control barrier function based quadratic programs for safety critical systems. IEEE Trans Automat Contr. 2017;62(8):3861–3876. doi: 10.1109/TAC.2016.2638961
  • He CR, Orosz G. Safety guaranteed connected cruise control. 21st IEEE International Conference on Intelligent Transportation Systems; Maui (HI). IEEE; 2018. p. 549–554.
  • Kousik S, Vaskov S, Johnson-Roberson M, et al. Safe trajectory synthesis for autonomous driving in unforeseen environments. Dynamic Systems and Control Conference; Tysons (VA). ASME; 2017. p. V001T44A005–V001T44A005.
  • Kousik S, Vaskov S, Bu F, et al. Bridging the gap between safety and real-time performance in receding-horizon trajectory design for mobile robots. arXiv preprint arXiv:180906746. 2018.
  • Vaskov S, Sharma U, Kousik S, et al. Guaranteed safe reachability-based trajectory design for a high-fidelity model of an autonomous passenger vehicle. American Conference on Control; Philadelphia (PA). IEEE; 2019. p. 705–710.
  • Vaskov S, Kousik S, Larson H, et al. Towards provably not-at-fault control of autonomous robots in arbitrary dynamic environments. Robotics: Science and Systems; 2019. arXiv:1902.02851.
  • Ozay N, Tabuada P. Guest editorial: special issue on formal methods in control. Discrete Event Dyn Syst. 2017;27(2):205–208. doi: 10.1007/s10626-017-0246-9
  • Chou G, Sahin YE, Yang L, et al. Using control synthesis to generate corner cases: a case study on autonomous driving. IEEE Trans Comput Aided Des Integr Circuits Syst. 2018;37(11):2906–2917. doi: 10.1109/TCAD.2018.2858464
  • Annpureddy Y, Liu C, Fainekos G, et al. S-taliro: a tool for temporal logic falsification for hybrid systems. International Conference on Tools and Algorithms for the Construction and Analysis of Systems; Saarbrücken, Germany. Springer; 2011. p. 254–257.
  • Cavada R, Cimatti A, Dorigatti M, et al. The nuXmv symbolic model checker. International Conference on Computer Aided Verification; Vienna, Austria. Springer; 2014. p. 334–342.
  • Donzé A. Breach, a toolbox for verification and parameter synthesis of hybrid systems. International Conference on Computer Aided Verification; Edinburgh. Springer; 2010. p. 167–170.
  • Rungger M, Zamani M. SCOTS: a tool for the synthesis of symbolic controllers. 19th International Conference on Hybrid Systems: Computation and Control; Vienna, Austria. ACM; 2016. p. 99–104.
  • Filippidis I, Dathathri S, Livingston SC, et al. Control design for hybrid systems with TuLiP: the temporal logic planning toolbox. IEEE Conference on Control Applications; Buenos Aires, Argentina. IEEE; 2016. p. 1030–1041.
  • Smith SW, Nilsson P, Ozay N. Interdependence quantification for compositional control synthesis with an application in vehicle safety systems. 55th IEEE Conference on Decision and Control; Las Vegas (NV). IEEE; 2016. p. 5700–5707.
  • Nilsson P, Ozay N. Provably-correct compositional synthesis of vehicle safety systems. Safe, Autonomous and Intelligent Vehicles; Cham, Switzerland. Springer; 2019. p. 97–122.
  • Mazo M, Davitian A, Tabuada P. Pessoa: a tool for embedded controller synthesis. International Conference on Computer Aided Verification; Edinburgh. Springer; 2010. p. 566–569.
  • Blanchini F. Set invariance in control. Automatica. 1999;35(11):1747–1767. doi: 10.1016/S0005-1098(99)00113-2
  • Jin X, Deshmukh JV, Kapinski J, et al. Powertrain control verification benchmark. 17th International Conference on Hybrid Systems: Computation and Control; Berlin, Germany. ACM; 2014. p. 253–262.
  • Yang L, Karnik A, Pence B, et al. Fuel cell thermal management: modeling, specifications and correct-by-construction control synthesis. American Control Conference; Seattle (WA). IEEE; 2017. p. 1839–1846.
  • Seshia SA, Sadigh D, Sastry SS. Formal methods for semi-autonomous driving. 52nd ACM/EDAC/IEEE Design Automation Conference; San Francisco (CA). IEEE; 2015. p. 1–5.
  • Sadigh D, Sastry SS, Seshia SA. Verifying robustness of human-aware autonomous cars. 2nd IFAC Conference on Cyber-Physical and Human Systems; Miami (FL). IEEE; 2019. Vol. 51, p. 131–138.
  • Coogan S, Gol EA, Arcak M, et al. Traffic network control from temporal logic specifications. IEEE Trans Control Netw Syst. 2016;3(2):162–172. doi: 10.1109/TCNS.2015.2428471
  • Coogan S, Arcak M, Belta C. Formal methods for control of traffic flow: automated control synthesis from finite-state transition models. IEEE Control Syst Mag. 2017;37(2):109–128. doi: 10.1109/MCS.2016.2643259
  • Tuncali CE, Fainekos G, Ito H, et al. Simulation-based adversarial test generation for autonomous vehicles with machine learning components. IEEE Intelligent Vehicles Symposium; Changshu, China. IEEE; 2018. p. 1555–1562.
  • Cook JA, Kolmanovsky IV, McNamara D, et al. Control, computing and communications: technologies for the twenty-first century model T. Proc IEEE. 2007;95(2):334–355. doi: 10.1109/JPROC.2006.888384
  • Vahidi A, Sciarretta A. Energy saving potentials of connected and automated vehicles. Transp Res Part C. 2018;95:822–843. doi: 10.1016/j.trc.2018.09.001
  • Vahidi A, Sciarretta A. Energy-efficient driving of road vehicles: toward cooperative, connected, and automated mobility. Cham: Springer; 2020.
  • Guanetti J, Kim Y, Borrelli F. Control of connected and automated vehicles: state of the art and future challenges. Annu Rev Control. 2018;45:18–40. doi: 10.1016/j.arcontrol.2018.04.011
  • Eriksson L, Nielsen L. Modeling and control of engines and drivelines. Hoboken (NJ): John Wiley; 2014.
  • Xu S, Li SE, Zhang X, et al. Fuel-optimal cruising strategy for road vehicles with step-gear mechanical transmission. IEEE Trans Intell Transp Syst. 2015;16(6):3496–3507. doi: 10.1109/TITS.2015.2459722
  • Shieh SY, Ersal T, Peng H. Pulse-and-glide operation for parallel hybrid electric vehicles with step-gear transmission in automated car-following scenario with ride comfort consideration. American Control Conference; Philadelphia (PA). IEEE; 2019. p. 959–964.
  • He CR, Maurer H, Orosz G. Fuel consumption optimization of heavy-duty vehicles with grade, wind, and traffic information. ASME J Comput Nonlinear Dyn. 2016;11(6):061011.
  • Katsargyri GE, Kolmanovsky I, Michelini J, et al. Path dependent receding horizon control policies for hybrid electric vehicles. 18th IEEE International Conference on Control Applications; Saint Petersburg, Russia. IEEE; 2009. p. 607–612.
  • Szwabowski SJ, MacNeille P, Kolmanovsky IV, et al. In-vehicle ambient condition sensing based on wireless internet access. SAE; 2010. (SAE Technical Paper 2010-01-0461).
  • Cho D, Gupta R, Dai E, et al. Launch performance optimization of gtdi-dct powertrain. SAE Int J Engines. 2015;8(3):1398–1407. doi: 10.4271/2015-01-1111
  • Park H, Gupta R, Dai E, et al. Quantifying performance of a connected vehicle by optimal oontrol. 4th IFAC Workshop Engine Powertrain Control, Simul Model. 2015;48(15):328–334.
  • Schoeggl P, Ramschak E. Vehicle driveability assessment using neural networks for development, calibration and quality tests. SAE; 2000. (SAE Technical Paper 2000-01-0702).
  • He CR, Qin WB, Ozay N, et al. Optimal gear shift schedule design for automated vehicles: hybrid system based analytical approach. IEEE Trans Control Syst Technol. 2018;26(6):2078–2090. doi: 10.1109/TCST.2017.2747506
  • Taheri E, Gusikhin O, Kolmanovsky I. Failure prognostics for in-tank fuel pumps of the returnless fuel systems. ASME 2016 Dynamic Systems and Control Conference; Minneapolis (MN). IEEE; 2016. p. V001T12A002–V001T12A002.
  • Breschi V, Kolmanovsky I, Bemporad A. Cloud-aided collaborative estimation by admm-rls algorithms for connected diagnostics and prognostics. American Control Conference; Milwaukee (WI). IEEE; 2018. p. 2727–2732.
  • Amini MR, Gong X, Feng Y, et al. Sequential eco-optimization of speed, thermal load, and power split in connected HEVs. American Control Conference; Philadelphia (PA). IEEE; 2019. p. 4614–4620.
  • Kim Y, Salvi A, Stefanopoulou A, et al. Reducing soot emissions in a diesel series hybrid electric vehicle using a power rate constraint map. IEEE Trans Veh Technol. 2015;64(1):2–12. doi: 10.1109/TVT.2014.2321346
  • Ersal T, Brudnak M, Salvi A, et al. Development and model-based transparency analysis of an internet-distributed hardware-in-the-loop simulation platform. Mechatronics. 2011;21(1):22–29. doi: 10.1016/j.mechatronics.2010.08.002
  • Andersson A, Nyberg P, Sehammar H, et al. Vehicle powertrain test bench co-simulation with a moving base simulator using a pedal robot. SAE Int J Passenger Cars – Electron Electr Syst. 2013;6(1):169–179. doi: 10.4271/2013-01-0410
  • Kim Y, Salvi A, Siegel JB, et al. Hardware-in-the-loop validation of a power management strategy for hybrid powertrains. Control Eng Pract. 2014;29:277–286.
  • Zhang Y, Lu S, Yang Y, et al. Internet-distributed vehicle-in-the-loop simulation for hevs. IEEE Trans Veh Technol. 2018;67(5):3729–3739. doi: 10.1109/TVT.2018.2803088
  • Ersal T, Gillespie RB, Brudnak M, et al. Effect of coupling point selection on distortion in internet-distributed hardware-in-the-loop simulation. Int J Veh Des. 2013;61(1–4):67–85. doi: 10.1504/IJVD.2013.050840
  • Ersal T, Brudnak MJ, Salvi A, et al. An iterative learning control approach to improving fidelity in internet-distributed hardware-in-the-loop simulation. J Dyn Syst Meas Control. 2014;136(6):061012–1–061012–8. doi: 10.1115/1.4027868
  • Zheng Y, Brudnak MJ, Jayakumar P, et al. A predictor based framework for delay compensation in networked closed-loop systems. IEEE ASME Trans Mechatron. 2018;23(5):2482–2493. doi: 10.1109/TMECH.2018.2864722
  • Molnár TG, Qin WB, Insperger T, et al. Application of predictor feedback to compensate time delays in connected cruise control. IEEE Trans Intell Transp Syst. 2018;19(2):545–559. doi: 10.1109/TITS.2017.2754240
  • Ploeg J, Van De Wouw N, Nijmeijer H. Lp string stability of cascaded systems: application to vehicle platooning. IEEE Trans Control Syst Technol. 2014;22(2):786–793. doi: 10.1109/TCST.2013.2258346
  • Feng S, Zhang Y, Li SE, et al. String stability for vehicular platoon control: definitions and analysis methods. Annu Rev Control. 2019;47:81–97. doi: 10.1016/j.arcontrol.2019.03.001
  • Gunter G, Gloudemans D, Stern RE, et al. Are commercially implemented adaptive cruisecontrol systems string stable? 10th ACM/IEEE International Conference on Cyber-Physical Systems; 2019. arXiv:1905.02108v1.
  • Besselink B, Johansson KH. String stability and a delay-based spacing policy for vehicle platoons subject to disturbances. IEEE Trans Automat Contr. 2017;62(9):4376–4391. doi: 10.1109/TAC.2017.2682421
  • Davis LC. Effect of adaptive cruise control systems on traffic flow. Phys Rev E. 2004;69(6):066110. doi: 10.1103/PhysRevE.69.066110
  • Stern RE, Cui S, Delle Monache ML, et al. Dissipation of stop-and-go waves via control of autonomous vehicles: field experiments. Transp Res Part C. 2018;89:205–221. doi: 10.1016/j.trc.2018.02.005
  • Qin WB, Orosz G. Experimental validation of string stability for connected vehicles subject to information delay. IEEE Trans Control Syst Technol; 2020. Available from: https://doi.org/10.1109/TCST.2019.2900609
  • Ge JI, Orosz G. Dynamics of connected vehicle systems with delayed acceleration feedback. Transp Res Part C. 2014;46:46–64. doi: 10.1016/j.trc.2014.04.014
  • Orosz G. Connected cruise control: modelling, delay effects, and nonlinear behaviour. Veh Syst Dyn. 2016;54(8):1147–1176. doi: 10.1080/00423114.2016.1193209
  • Ge JI, Avedisov SS, He CR, et al. Experimental validation of connected automated vehicle design among human-driven vehicles. Transp Res Part C. 2018;91:335–352. doi: 10.1016/j.trc.2018.04.005
  • Zhang L, Orosz G. Motif-based design for connected vehicle systems in presence of heterogeneous connectivity structures and time delays. IEEE Trans Intell Transp Syst. 2016;17(6):1638–1651. doi: 10.1109/TITS.2015.2509782
  • Qin WB, Orosz G. Experimental validation on connected cruise control with flexible connectivity topologies. IEEE ASME Trans Mechatron. 2019;24(6):2791–2802. doi: 10.1109/TMECH.2019.2943501
  • Milanes V, Shladover SE, Spring J, et al. Cooperative adaptive cruise control in real traffic situations. IEEE Trans Intell Transp Syst. 2014;15(1):296–305. doi: 10.1109/TITS.2013.2278494
  • Shladover SE, Nowakowski C, Lu XY, et al. Cooperative adaptive cruise control definitions and operating concepts. Transp Res Rec. 2015;2489:145–152. doi: 10.3141/2489-17
  • Zheng Y, Li SE, Wang J, et al. Stability and scalability of homogeneous vehicular platoon: study on the influence of information flow topologies. IEEE Trans Intell Transp Syst. 2016;17(1):14–26. doi: 10.1109/TITS.2015.2402153
  • Zheng Y, Li SE, Li K, et al. Distributed model predictive control for heterogeneous vehicle platoons under unidirectional topologies. IEEE Trans Control Syst Technol. 2017;25(3):899–910. doi: 10.1109/TCST.2016.2594588
  • Öncü S, Ploeg J, Van De Wouw N, et al. Cooperative adaptive cruise control: network-aware analysis of string stability. IEEE Trans Intell Transp Syst. 2014;15(4):1527–1537. doi: 10.1109/TITS.2014.2302816
  • Van Nunen E, Reinders J, Semsar-Kazerooni E, et al. String stable model predictive cooperative adaptive cruise control for heterogeneous platoons. IEEE Trans Intell Veh. 2019;4(2):186–196. doi: 10.1109/TIV.2019.2904418
  • Alam A, Besselink B, Turri V, et al. Heavy-duty vehicle platooning for sustainable freight transportation: a cooperative method to enhance safety and efficiency. IEEE Control Syst Mag. 2015;35(6):34–56. doi: 10.1109/MCS.2015.2471046
  • Wang M, Daamen W, Hoogendoorn SP, et al. Rolling horizon control framework for driver assistance systems. Part II: cooperative sensing and cooperative control. Transp Res Part C. 2014;40:290–311. doi: 10.1016/j.trc.2013.11.024
  • Turri V, Besselink B, Johansson KH. Cooperative look-ahead control for fuel-efficient and safe heavy-duty vehicle platooning. IEEE Trans Control Syst Technol. 2016;25(1):12–28. doi: 10.1109/TCST.2016.2542044
  • Rios-Torres J, Malikopoulos AA. A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps. IEEE Trans Intell Transp Syst. 2017;18(5):1066–1077. doi: 10.1109/TITS.2016.2600504
  • Malikopoulos AA, Cassandras CG, Zhang YJ. A decentralized energy-optimal control framework for connected automated vehicles at signal-free intersections. Automatica. 2018;93:244–256. doi: 10.1016/j.automatica.2018.03.056
  • Wang M, Daamen W, Hoogendoorn SP, et al. Cooperative car-following control: distributed algorithm and impact on moving jam features. IEEE Trans Intell Transp Syst. 2016;17(5):1459–1471. doi: 10.1109/TITS.2015.2505674
  • Talebpour A, Mahmassani HS. Influence of connected and autonomous vehicles on traffic flow stability and throughput. Transp Res Part C. 2016;71:143–163. doi: 10.1016/j.trc.2016.07.007
  • Wu C, Bayen AM, Mehta A. Stabilizing traffic with autonomous vehicles. IEEE International Conference on Robotics and Automation; Brisbane, Australia. IEEE; 2018. p. 6012–6018.
  • Avedisov SS, Bansal G, Orosz G. Impact of connected automated vehicles on traffic dynamics. IEEE Trans Intell Transp Syst. 2019.
  • Masoud N, Jayakrishnan R. Autonomous or driver-less vehicles: implementation strategies and operational concerns. Transp Res Part E. 2017;108:179–194. doi: 10.1016/j.tre.2017.10.011
  • Lloret-Batlle R, Masoud N, Nam D. Peer-to-peer ridesharing with ride-back on high-occupancy-vehicle lanes: toward a practical alternative mode for daily commuting. Transp Res Rec. 2017;2668(1):21–28. doi: 10.3141/2668-03
  • Regue R, Masoud N, Recker W. Car2work: a shared mobility concept to connect commuters with workplaces. Transp Res Record: J Transp Res Board. 2016;2542:102–110. doi: 10.3141/2542-12
  • Hamari J, Sjöklint M, Ukkonen A. The sharing economy: why people participate in collaborative consumption. J Assoc Inf Sci Technol. 2016;67(9):2047–2059. doi: 10.1002/asi.23552
  • Heinrichs H. Sharing economy: a potential new pathway to sustainability. GAIA-Ecol Perspect Sci Soc. 2013;22(4):228–231.
  • Cohen B, Kietzmann J. Ride on! mobility business models for the sharing economy. Organ Environ. 2014;27(3):279–296. doi: 10.1177/1086026614546199
  • Lloret-Batlle R, Jayakrishnan R. Envy-minimizing pareto efficient intersection control with brokered utility exchanges under user heterogeneity. Transp Res Part B: Methodological. 2016;94:22–42. doi: 10.1016/j.trb.2016.08.014
  • Lloret-Batlle R, Jayakrishnan R. Envy-free pricing for collaborative consumption of supply in transportation systems. Transp Res Procedia. 2017;23:772–789. doi: 10.1016/j.trpro.2017.05.043
  • Masoud N, Lloret-Batlle R, Jayakrishnan R. Using bilateral trading to increase ridership and user permanence in ridesharing systems. Transp Res Part E. 2017;102:60–77. doi: 10.1016/j.tre.2017.04.007
  • Lloret-Batlle R, Jayakrishnan R. Study of a dynamic cooperative trading queue routing control scheme for freeways and facilities with parallel queues.97th Annual Meeting of the Transportation Research Board; Washington (DC). SAGE Publishing; 2018.
  • Sun X, Yin Y. Behaviorally stable vehicle platooning for energy savings. Transp Res Part C. 2019;99:37–52. doi: 10.1016/j.trc.2018.12.017
  • Malik P, Jin WL, Lloret-Batlle R, et al. A unifiable multi-commodity kinematic wave model for traffic systems with tradable right-of-way. 98th Annual Meeting of the Transportation Research Board; Washington (DC). SAGE Publishing; 2019.

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