Intention-Based Lane Changing and Lane Keeping Haptic Guidance Steering System

Haptic guidance in a shared steering assistance system has drawn significant attention in intelligent vehicle fields, owing to its mutual communication ability for vehicle control. By exerting continuous torque on the steering wheel, both the driver and support system can share lateral control of the vehicle. However, current haptic guidance steering systems demonstrate some deficiencies in assisting lane changing. This study explored a new steering interaction method, including the design and evaluation of an intention-based haptic shared steering system. Such an intention-based method can support both lane keeping and lane changing assistance, by detecting a driver lane change intention. By using a deep learning-based method to model a driver decision timing regarding lane crossing, an adaptive gain control method was proposed for realizing a steering control system. An intention consistency method was proposed to detect whether the driver and the system were acting towards the same target trajectories and to accurately capture the driver intention. A driving simulator experiment was conducted to test the system performance. Participants were required to perform six trials with assistive methods and one trial without assistance. The results demonstrated that the supporting system decreased the lane departure risk in the lane keeping tasks and could support a fast and stable lane changing maneuver.

[1]  N. Montés,et al.  Lane changing using s-series clothoidal approximation and dual-rate based on Bezier points to controlling vehicle , 2004 .

[2]  Kimihiko Nakano,et al.  Relationship Between Gaze Behavior and Steering Performance for Driver–Automation Shared Control: A Driving Simulator Study , 2019, IEEE Transactions on Intelligent Vehicles.

[3]  Yingshi Guo,et al.  Improving the User Acceptability of Advanced Driver Assistance Systems Based on Different Driving Styles: A Case Study of Lane Change Warning Systems , 2020, IEEE Transactions on Intelligent Transportation Systems.

[4]  Dario D. Salvucci,et al.  The time course of a lane change: Driver control and eye-movement behavior , 2002 .

[5]  Keith Redmill,et al.  Systems for Safety and Autonomous Behavior in Cars: The DARPA Grand Challenge Experience , 2007, Proceedings of the IEEE.

[6]  Ryota Nishimura,et al.  Haptic Shared Control in Steering Operation Based on Cooperative Status Between a Driver and a Driver Assistance System , 2015, HRI 2015.

[7]  Johannes Fürnkranz,et al.  Time-to-lane-change prediction with deep learning , 2017, 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC).

[8]  Mark Mulder,et al.  The effect of haptic guidance on curve negotiation behavior of young, experienced drivers , 2008, 2008 IEEE International Conference on Systems, Man and Cybernetics.

[9]  Mark Mulder,et al.  Correct and faulty driver support from shared haptic control during evasive maneuvers , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[10]  Mark Mulder,et al.  Haptic shared control: smoothly shifting control authority? , 2012, Cognition, Technology & Work.

[11]  Nicola Vitiello,et al.  Intention-Based EMG Control for Powered Exoskeletons , 2012, IEEE Transactions on Biomedical Engineering.

[12]  Errol R. Hoffmann,et al.  Steering Reversals as a Measure of Driver Performance and Steering Task Difficulty , 1975 .

[13]  Hermann Winner,et al.  Three Decades of Driver Assistance Systems: Review and Future Perspectives , 2014, IEEE Intelligent Transportation Systems Magazine.

[14]  Takahiro Wada,et al.  Control Authority Transfer Method for Automated-to-Manual Driving Via a Shared Authority Mode , 2018, IEEE Transactions on Intelligent Vehicles.

[15]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[16]  Mark Mulder,et al.  Sharing Control With Haptics , 2012, Hum. Factors.

[17]  Yasuhisa Hasegawa,et al.  Intention-based walking support for paraplegia patients with Robot Suit HAL , 2007, Adv. Robotics.

[18]  Andreas Lüdtke,et al.  Developing a model of driver's uncertainty in lane change situations for trustworthy lane change decision aid systems , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[19]  Mark Mulder,et al.  Balancing safety and support: Changing lanes with a haptic lane-keeping support system , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[20]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[21]  Kimihiko Nakano,et al.  The Effect of a Haptic Guidance Steering System on Fatigue-Related Driver Behavior , 2017, IEEE Transactions on Human-Machine Systems.

[22]  Firas Lethaus,et al.  A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data , 2013, Neurocomputing.

[23]  Seiichi Mita,et al.  Bézier curve based path planning for autonomous vehicle in urban environment , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[24]  Mathias Perrollaz,et al.  Learning-based approach for online lane change intention prediction , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[25]  Philippe Chevrel,et al.  Shared Steering Control Between a Driver and an Automation: Stability in the Presence of Driver Behavior Uncertainty , 2013, IEEE Transactions on Intelligent Transportation Systems.

[26]  Zheng Wang Analysis and Modeling of Driver Behavior with Integrated Feedback of Visual and Haptic Information Under Shared Control , 2021, ArXiv.

[27]  Hermann Winner,et al.  A maneuver-based lane change assistance system , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[28]  Jooyoung Park,et al.  Prediction of Driver’s Intention of Lane Change by Augmenting Sensor Information Using Machine Learning Techniques , 2017, Sensors.

[29]  Yasuhisa Hasegawa,et al.  Intention-based walking support for paraplegia patients with Robot Suit HAL , 2007 .

[30]  Bo Yang,et al.  Time to lane change and completion prediction based on Gated Recurrent Unit Network , 2019, 2019 IEEE Intelligent Vehicles Symposium (IV).

[31]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[32]  Mohan M. Trivedi,et al.  On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes , 2009, IEEE Transactions on Intelligent Transportation Systems.

[33]  Shirou Nakano,et al.  Preview-scheduled steering assistance control for co-piloting vehicle: a human-like methodology , 2019, Vehicle System Dynamics.

[34]  Saïd Mammar,et al.  Time to line crossing for lane departure avoidance: a theoretical study and an experimental setting , 2006, IEEE Transactions on Intelligent Transportation Systems.

[35]  T. Inagaki,et al.  Haptic steering direction guidance for pedestrian-vehicle collision avoidance , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[36]  Jean-Michel Hoc,et al.  Analysis of Human-Machine Cooperation When Driving with Different Degrees of Haptic Shared Control , 2014, IEEE Transactions on Haptics.

[37]  E. Williams Experimental Designs Balanced for the Estimation of Residual Effects of Treatments , 1949 .

[38]  Shiho Kim,et al.  Path generation and tracking based on a Bézier curve for a steering rate controller of autonomous vehicles , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[39]  S. G. Hart,et al.  Development of NASA-TLX(Task Load Index) , 1988 .

[40]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.