Detection of Driving Capability Degradation for Human-Machine Cooperative Driving

Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.

[1]  Hai-Jun Su,et al.  A feedforward and feedback integrated lateral and longitudinal driver model for personalized advanced driver assistance systems , 2018 .

[2]  Sebastian Thrun,et al.  Autonomous Driving: Context and State-of-the-Art , 2012 .

[3]  Saïd Mammar,et al.  Driver Steering Assistance for Lane-Departure Avoidance Based on Hybrid Automata and Composite Lyapunov Function , 2010, IEEE Transactions on Intelligent Transportation Systems.

[4]  Rajeev Motwani,et al.  Maintaining variance and k-medians over data stream windows , 2003, PODS.

[5]  Junqiang Xi,et al.  A Learning-Based Approach for Lane Departure Warning Systems With a Personalized Driver Model , 2017, IEEE Transactions on Vehicular Technology.

[6]  Wolfgang Birk,et al.  A driver-distraction-based lane-keeping assistance system , 2007 .

[7]  Josef Nilsson,et al.  Safe Transitions From Automated to Manual Driving Using Driver Controllability Estimation , 2015, IEEE Transactions on Intelligent Transportation Systems.

[8]  Keqiang Li,et al.  A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study , 2019 .

[9]  Iyad Rahwan,et al.  The social dilemma of autonomous vehicles , 2015, Science.

[10]  Feng Guo,et al.  Keep your eyes on the road: young driver crash risk increases according to duration of distraction. , 2014, The Journal of adolescent health : official publication of the Society for Adolescent Medicine.

[11]  Junmin Wang,et al.  A Driver Steering Model With Personalized Desired Path Generation , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[12]  Qi Sun,et al.  Robust Distributed Consensus Control of Uncertain Multiagents Interacted by Eigenvalue-Bounded Topologies , 2020, IEEE Internet of Things Journal.

[13]  Feng Gao,et al.  Distributed H∞ Control Of Platoon Interacted by Switching and Undirected Topology , 2020 .

[14]  Feng Gao,et al.  Robust Control of Heterogeneous Vehicular Platoon with Non-Ideal Communication , 2019, Electronics.

[15]  Ying Li,et al.  Automatic Dangerous Driving Intensity Analysis for Advanced Driver Assistance Systems From Multimodal Driving Signals , 2018, IEEE Sensors Journal.

[16]  John D Lee,et al.  Combining cognitive and visual distraction: less than the sum of its parts. , 2010, Accident; analysis and prevention.

[17]  Weihua Sheng,et al.  A driver assistance framework based on driver drowsiness detection , 2016, 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER).

[18]  Feng Gao,et al.  Test Scenario Generation and Optimization Technology for Intelligent Driving Systems , 2022, IEEE Intelligent Transportation Systems Magazine.

[19]  Hongbo Wang,et al.  Shared control for lane departure prevention based on the safe envelope of steering wheel angle , 2017 .

[20]  Karel Brookhuis,et al.  Issues arising from the HASTE experiments , 2005 .

[21]  Meixin Zhu,et al.  Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study , 2018, Transportation Research Part C: Emerging Technologies.

[22]  B. Jolly,et al.  Bigger is better, but not for everyone. NHTSA. National Highway Traffic Safety Administration. , 1997, Annals of emergency medicine.

[23]  James R. Sayer,et al.  Distracted Driving Performance Measures , 2015 .

[24]  Seung-Hyun Kong,et al.  Driver Status Monitoring Systems for Smart Vehicles Using Physiological Sensors: A safety enhancement system from automobile manufacturers , 2016, IEEE Signal Processing Magazine.

[25]  N Merat,et al.  Leading to distraction: Driver distraction, lead car, and road environment. , 2016, Accident; analysis and prevention.

[26]  Keiichi Uchimura,et al.  Driver Inattention Monitoring System for Intelligent Vehicles: A Review , 2009, IEEE Transactions on Intelligent Transportation Systems.

[27]  Chouki Sentouh,et al.  Driver-Automation Cooperation Oriented Approach for Shared Control of Lane Keeping Assist Systems , 2019, IEEE Transactions on Control Systems Technology.

[28]  Sören Hohmann,et al.  Cooperative Shared Control Driver Assistance Systems Based on Motion Primitives and Differential Games , 2017, IEEE Transactions on Human-Machine Systems.

[29]  Fangyu Wu,et al.  Connections between classical car following models and artificial neural networks , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[30]  Chouki Sentouh,et al.  Human-Machine Interaction in Automated Vehicle: The ABV Project , 2014 .

[31]  Murat Kayri,et al.  Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data , 2016 .

[32]  Chouki Sentouh,et al.  Online adaptation of the Level of Haptic Authority in a lane keeping system considering the driver’s state , 2017, Transportation Research Part F: Traffic Psychology and Behaviour.

[33]  Ilya V. Kolmanovsky,et al.  Visual-Manual Distraction Detection Using Driving Performance Indicators With Naturalistic Driving Data , 2018, IEEE Transactions on Intelligent Transportation Systems.