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SCIENCE CHINA Information Sciences, Volume 63 , Issue 9 : 190201(2020) https://doi.org/10.1007/s11432-019-2987-x

Driver-automation shared steering control for highly automated vehicles

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  • ReceivedOct 17, 2019
  • AcceptedJul 9, 2020
  • PublishedAug 12, 2020

Abstract

A model predictive control (MPC)-based shared steering framework for intelligent vehicles is proposed in this paper. The road boundary and vehicle stability boundary are regarded as the safe envelope, and the tradeoff between the freedom of driver operation and safety assurance of intelligent vehicles is made within this safe envelope. Under this cooperative steering framework, the reliability of drivers is analyzed in dangerous situations and in the predictive time domain, and two improved schemes are proposed. Under the two improved schemes, the weight of the control objective can be adaptively changed according to the results of the threat assessment and predetermined strategy. At the same time, an evaluation index named control intervention rate and risk rate is proposed to evaluate the designed human-vehicle cooperation scheme. The simulation results show that the performance of the two improved schemes in ensuring the safety of intelligent vehicles has been improved.


Acknowledgment

This work was supported by National Natural Science Foundation of China (Grant Nos. U19A2069, 61790563, U1664263), Project of the Education Department of Jilin Province (Grant No. JJKH20190165KJ), and Project of Development and Reform Commission of Jilin Province (Grant No. 2019C036-5).


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  • Figure 1

    A 2-DOF vehicle model.

  • Figure 2

    (Color online) Schematic diagram of the intelligent vehicle shared steering control.

  • Figure 3

    (Color online) Control block diagram of the shared steering vehicle.

  • Figure 6

    Simulation results of low-risk lane change. (a) Vehicle trajectory; (b) front wheel angle; (c) sideslip angle;protect łinebreak (d) yaw rate.

  • Figure 7

    Comparison of low-risk lane change test results.

  • Figure 10

    Comparison of high-risk scenario test results.

  • Table 1  

    Table 1Driver types and characteristics

    Driver type Driver characteristics
    D1 skillful, careful, smooth with preview
    D2 skillful, racy, direct with preview
    D3 without preview
    D4 untrained, racy, direct with preview
  • Table 2  

    Table 2Performance of three schemes in low-risk scenarios

    Index NO AUTO FUZZY HORIZON
    Average intervention rate (%) 0.4482 0.4531 1.3453
  • Table 3  

    Table 3High-risk virtual tests

    No. Test scenario Driver type Velocity (km/h) Friction coefficient
    1 slalom D1 80 0.75
    2 double-lane change D1 100 0.55
    3 obstacle avoidance D1 50 0.55
    4 slalom D2 85 0.75
    5 double-lane change D4 85 0.55
    6 double-lane change D3 75 0.55
  • Table 4  

    Table 4High-risk condition performance

    Index NO AUTO FUZZY HORIZON
    Average hazard rate (%) 16.7056 7.1562 4.3529

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