On the design of a human–robot interaction strategy for commercial vehicle driving based on human cognitive parameters

A proper design of human–robot interaction strategies based on human cognitive factors can help to compensate human limitations for safety purposes. This work is focused on the development of a human–robot interaction system for commercial vehicle (Renault Twizy) driving, that uses driver cognitive parameters to improve driver’s safety during day and night tasks. To achieve this, eye blink behavior measurements are detected using a convolutional neural network, which is capable of operating under variable illumination conditions using an infrared camera. Percentage of eye closure measure values along with blink frequency are used to infer diver’s sleepiness level. The use of such algorithm is validated with experimental tests for subjects under different sleep-quality conditions. Additional cognitive parameters are also analyzed for the human–robot interaction system such as driver sleep quality, distraction level, stress level, and the effects related to not wearing glasses. Based on such driver cognitive state parameters, a human–robot interaction strategy is proposed to limit the speed of a Renault Twizy vehicle by intervening its acceleration and braking system. The proposed human–robot interaction strategy can increase safety during driving tasks for both users and pedestrians.