Human–Machine Collaboration for Automated Driving Using an Intelligent Two‐Phase Haptic Interface

Chen Lv,Yifan Zhao,Yang Xing,Chao Huang,Yahui Liu,Dongpu Cao,Yutong Li

doi:10.1002/aisy.202000229

Abstract

Prior to realizing fully autonomous driving, human intervention is periodically required to guarantee vehicle safety. This poses a new challenge in human–machine interaction, particularly during the control authority transition from automated functionality to a human driver. Herein, this challenge is addressed by proposing an intelligent haptic interface based on a newly developed two‐phase human–machine interaction model. The intelligent haptic torque is applied to the steering wheel and switches its functionality between predictive guidance and haptic assistance according to the varying state and control ability of human drivers. This helps drivers gradually resume manual control during takeover. The developed approach is validated by conducting vehicle experiments with 26 participants. The results suggest that the proposed method effectively enhances the driving state recovery and control performance of human drivers during takeover compared with an existing approach. Thus, this new method further improves the safety and smoothness of human–machine interaction in automated vehicles.

Highlights

Introduction control performance worsenedThe increase in takeover time wasThe development of automated roadway vehicles has generated increasing attention from both academia and industry in recent found to be heavily related to the level of cognitive workload occupied by secondary tasks.[14,15] Different modalities of the takeover request (TOR) signal were investigated in one study,[16] and the results showed that years
The feasibility and effectiveness of the proposed intelligent haptic takeover control were investigated by conducting experiments in an automated vehicle (Figure 4a,b) involving 26 participants engaged in 2 tasks
The allowed driver control authority αref, degree of driver intervention or control performance α, haptic guidance torque Thpt, torque applied to the steering wheel by the driver TH, and yaw rate ψ⋅ of the vehicle were recorded for each participant over time

Summary

Introduction

Introduction control performance worsenedThe increase in takeover time wasThe development of automated roadway vehicles has generated increasing attention from both academia and industry in recent found to be heavily related to the level of cognitive workload occupied by secondary tasks.[14,15] Different modalities of the TOR signal were investigated in one study,[16] and the results showed that years. The development of highly automated vehicles or users’ preferences for TOR modalities in highly automated semiautonomous vehicles, where the driving task is exchanged vehicles depended on the urgency of the driving situation. The periodically between human drivers and automated vehicle tech- impact of traffic density on the takeover process was explored, nologies, can be expected to precede the transition to fully autonomous vehicles.[1,2,3] The transition between human driving and automated driving modes represents a particular risk because and the results indicated that a high density of traffic flow would have a negative impact on both takeover time and post-takeover performance.[17] In addition, the driver’s readiness and takeover human drivers may be preoccupied with a nondriving activity ability were explored by modeling and estimation.[10,18,19,20]

Methods

Results

Conclusion