LPV lateral control for ADAS based on driver performance monitoring

Medero, Ariel; Sename, Olivier; Puig, Vicenç

doi:10.1016/j.ifacol.2022.07.207

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Interesting control strategies have been proposed in last years for novel ADAS strategies [ 5 ], such as Lane Keeping Assistance Systems (LKAS), Pedestrian Safety Systems (PSS), Collision Warning Systems (CWS), Cruise Control Systems (CCS), Night Vision Systems (NVS), etc. Recently, new developments focused on shared control between the vehicle and the driver have been proposed as a promise of a higher automated control level; for instance, the robust control system proposed in [ 6 ] for LKAS using a human-in-the-loop vehicle system or the linear parameter varying approach presented in [ 7 ] for vehicle lateral control incorporating driver performance estimations. In [ 8 ] it is established that the control benefits of any ADAS could be context dependent particularly by the traffic and necessarily it is required the use of a vehicle and driver monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors

Campos-Ferreira

Lozoya-Santos

Tudón-Martínez

et al. 2023

Sensors

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This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver’s health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle’s central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver’s heart condition and vehicular traffic have been found in this analysis.

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