Shared steering torque control for lane change assistance : a stochastic game-theoretic approach
The challenging issue of "human-machine co-pilot" opens up a new frontier to enhance driving safety. However, driver-machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering system, which degrades the system's path-tracking ability...
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Main Authors: | , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/84339 http://hdl.handle.net/10220/50063 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The challenging issue of "human-machine co-pilot" opens up a new frontier to enhance driving safety. However, driver-machine conflicts and uncertain driver/external disturbances are significant problems in cooperative steering system, which degrades the system's path-tracking ability and lowers driving safety. This paper proposes a novel stochastic game-based shared control framework to model steering torque interaction between driver and intelligent electric power steering (IEPS) system. A six-order driver-vehicle dynamic system including driver/external uncertainty is established for path-tracking. Then the affine-linear quadratic (LQ)-based path-tracking problem is proposed to model the maneuvers of driver and IEPS. Particularly, the feedback Nash and Stackelberg frameworks to the affine-quadratic problem are derived by stochastic dynamic programming (SDP). Two cases of co-pilot lane change driving scenarios are studied via computer simulation. The intrinsic relation between stochastic Nash and Stackelberg strategies are investigated based on the results. And the steering-in-the-loop (SIL) experiment reveals the potential of applying the proposed shared control framework to handle driver-IEPS conflicts and uncertain driver/external turbulence. Finally, the co-piloting experiments with human driver further demonstrate the rationality of the game-based pattern between both agents. |
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