Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN

Driving intention has been widely used in intelligent driver assistance systems, automated driving systems, and electric vehicle control strategies. The accuracy, practicality, and timeliness of the driving intention identification model are its key issues. In this paper, a novel driver’s braking intention identification model based on the Gaussian mixture-hidden Markov model (GHMM) and generalized growing and pruning radial basis function neural network (GGAP-RBFNN) is proposed to improve the identification accuracy of the model. The simplest brake pedal and vehicle speed data that are easily obtained from the vehicle are used as an observation sequence to improve practicality of the model. The data of the pressing brake pedal stage are used to identify the braking intention to improve the timeliness of the model. The experimental data collected from real vehicle tests are used for off-line training and online identification. The research results show that the accuracy of driver’s braking intention identification model based on the GHMM/GGAP-RBFNN hybrid model is 94.69% for normal braking and 95.57% for slight braking, which are, respectively, 26.55% and 17.72% higher than achieved by the GHMM. In addition, the data of the pressing brake pedal stage are used for intention identification, which is 1.2 s faster than that of the existing identification model based on the GHMM.

[1]  Xuan Zhao,et al.  Research on an equal power allocation electronic differential system for electric vehicle with dual-wheeled-motor front drive based on a wavelet controller , 2018 .

[2]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[3]  Marco Dozza,et al.  Counterfactual simulations applied to SHRP2 crashes: The effect of driver behavior models on safety benefit estimations of intelligent safety systems. , 2017, Accident; analysis and prevention.

[4]  Yugong Luo,et al.  Downhill safety assistance control for hybrid electric vehicles based on the downhill driver’s intention model , 2015 .

[5]  Hermann Winner,et al.  Predicting the driver's turn intentions at urban intersections using context-based indicators , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[6]  Alex Pentland,et al.  Modeling and Prediction of Human Behavior , 1999, Neural Computation.

[7]  Hyunsoo Kim,et al.  Development of Brake System and Regenerative Braking Cooperative Control Algorithm for Automatic-Transmission-Based Hybrid Electric Vehicles , 2015, IEEE Transactions on Vehicular Technology.

[8]  Wei Wang,et al.  Evaluating impacts of different longitudinal driver assistance systems on reducing multi-vehicle rear-end crashes during small-scale inclement weather. , 2017, Accident; analysis and prevention.

[9]  Chao Yang,et al.  Identification of a driver’s starting intention based on an artificial neural network for vehicles equipped with an automated manual transmission , 2016 .

[10]  Anders Lie,et al.  Commentary: Status of road safety in Asia , 2016, Traffic injury prevention.

[11]  Saic Commercial.  A Study on the Strategy of Braking Force Distribution for the Hybrid Braking System in Electric Vehicles Based on Braking Intention , 2009 .

[12]  Takashi Imamura,et al.  Study of Induction and Estimation Method for Driver's Intention by Using a Driving Simulator , 2013, 2013 IEEE International Conference on Systems, Man, and Cybernetics.

[13]  Narasimhan Sundararajan,et al.  An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  Cornelia Lex,et al.  Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust , 2018, Traffic injury prevention.

[15]  Shuo Cheng,et al.  Unified Brake Service by a Hierarchical Controller for Active Deceleration Control in an Electric and Automated Vehicle , 2017 .

[16]  Abdollah Homaifar,et al.  Driver intention estimation via discrete hidden Markov model , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[17]  A Lie,et al.  Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes. , 2015, Accident; analysis and prevention.

[18]  Seong-Whan Lee,et al.  Detection of Braking Intention during Simulated Driving Based on EEG Analysis: Online Study , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[19]  Fei Li,et al.  Design and control of a novel two-speed Uninterrupted Mechanical Transmission for electric vehicles , 2016 .

[20]  Yiming Ye,et al.  Composite braking AMT shift strategy for extended-range heavy commercial electric vehicle based on LHMM/ANFIS braking intention identification , 2018, Cluster Computing.

[21]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process , 2005, IEEE Transactions on Vehicular Technology.

[22]  Jessica B. Cicchino,et al.  Effectiveness of forward collision warning and autonomous emergency braking systems in reducing front-to-rear crash rates. , 2017, Accident; analysis and prevention.

[23]  G. Ruxton.  The unequal variance t-test is an underused alternative to Student's t-test and the Mann–Whitney U test , 2006 .

[24]  Wei Yuan,et al.  Multi-parameter prediction of drivers' lane-changing behaviour with neural network model. , 2015, Applied ergonomics.

[25]  Lifang Wang,et al.  Study on Requirements for Load Emulation of the Vehicle With an Electric Braking System , 2017, IEEE Transactions on Vehicular Technology.

[26]  Keqiang Li,et al.  Lane changing intention recognition based on speech recognition models , 2016 .

[27]  Hampton C. Gabler,et al.  Safety Benefits of Forward Collision Warning, Brake Assist, and Autonomous Braking Systems in Rear-End Collisions , 2012, IEEE Transactions on Intelligent Transportation Systems.

[28]  Fatemeh Afghah,et al.  Driver behavior modeling near intersections using support vector machines based on statistical feature extraction , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[29]  Huikang Wang,et al.  EEG-based emergency braking intention prediction for brain-controlled driving considering one electrode falling-off. , 2017, Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference.

[30]  Paramasivan Saratchandran,et al.  Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm , 1998, IEEE Trans. Neural Networks.

[31]  Erfu Yang,et al.  Bubble density gradient with laser detection: A wake-homing scheme for supercavitating vehicles , 2018, Advances in Mechanical Engineering.

[32]  Stefan Haufe,et al.  Electrophysiology-based detection of emergency braking intention in real-world driving , 2014, Journal of neural engineering.

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

[34]  Josef F. Krems,et al.  Adaptive fuzzy pattern classification for the online detection of driver lane change intention , 2017, Neurocomputing.

[35]  Kip Smith,et al.  Pedestrian injury mitigation by autonomous braking. , 2010, Accident; analysis and prevention.

[36]  Zhuoping Yu,et al.  Novel stability control strategy for distributed drive electric vehicle based on driver operation intention , 2016 .

[37]  Dong-Soo Kwon,et al.  Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion , 2017 .

[38]  Paul D. Walker,et al.  The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle , 2016 .

[39]  Marko Robnik-Sikonja,et al.  Theoretical and Empirical Analysis of ReliefF and RReliefF , 2003, Machine Learning.

[40]  Hyunsoo Kim,et al.  A Study of Fuel Economy Improvement in a Plug-in Hybrid Electric Vehicle using Engine on/off and Battery Charging Power Control Based on Driver Characteristics , 2015 .

[41]  Zhuoping Yu,et al.  Robust adaptive anti-slip regulation controller for a distributed-drive electric vehicle considering the driver’s intended driving torque , 2018 .

[42]  Klaus C. J. Dietmayer,et al.  Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios , 2009, IEEE Transactions on Intelligent Transportation Systems.

[43]  Yili Liu,et al.  Detecting Driver Normal and Emergency Lane-Changing Intentions With Queuing Network-Based Driver Models , 2015, Int. J. Hum. Comput. Interact..

[44]  Kai He,et al.  AMT downshifting strategy design of HEV during regenerative braking process for energy conservation , 2016 .

[45]  Cristina Olaverri-Monreal,et al.  How Electric Vehicles Affect Driving Behavioral Patterns , 2014, IEEE Intelligent Transportation Systems Magazine.

[46]  Christophe Bastien,et al.  Influence of vehicle secondary impact following an emergency braking on an unbelted occupant's neck, head and thorax injuries , 2013 .

[47]  Stefan Haufe,et al.  Detection of braking intention in diverse situations during simulated driving based on EEG feature combination , 2015, Journal of neural engineering.

[48]  Haobin Jiang,et al.  A Comparative Study of Clustering Analysis Method for Driver’s Steering Intention Classification and Identification under Different Typical Conditions , 2017 .

[49]  Ian J Reagan,et al.  Driver trust in five driver assistance technologies following real-world use in four production vehicles , 2017, Traffic injury prevention.