Advanced eNose-Driven Pedestrian Tracking Pipeline for Intelligent Car Driver Assisting System: Preliminary Results

From a biological point of view, alcohol human attentional impairment occurs before reaching a Blood Alcohol Content (BAC index) of 0.08% (0.05% under the Italian legislation), thus generating a significant impact on driving safety if the drinker subject is driving a car. Car drivers must keep a safe driving dynamic, having an unaltered physiological status while processing the surrounding information coming from the driving scenario (e.g., traffic signs, other vehicles and pedestrians). Specifically, the identification and tracking of pedestrians in the driving scene is a widely investigated problem in the scientific community. The authors propose a full, deep pipeline for the identification, monitoring and tracking of the salient pedestrians, combined with an intelligent electronic alcohol sensing system to properly assess the physiological status of the driver. More in detail, the authors propose an intelligent sensing system that makes a common air quality sensor selective to alcohol. A downstream Deep 1D Temporal Residual Convolutional Neural Network architecture will be able to learn specific embedded alcohol-dynamic features in the collected sensing data coming from the GHT25S air-quality sensor of STMicroelectronics. A parallel deep attention-augmented architecture identifies and tracks the salient pedestrians in the driving scenario. A risk assessment system evaluates the sobriety of the driver in case of the presence of salient pedestrians in the driving scene. The collected preliminary results confirmed the effectiveness of the proposed approach.

Face Masks as a Factor in Eventuality of Changes in Driving Safety

During the COVID-19, pandemics or worsening virus situation, taxi and regular-route bus drivers are recommended to work in medical masks. However, the quantitative and qualitative influence of wearing protective face masks on safety of driving vehicles has not been previously studied. Therefore, this became the objective of preliminary studies to determine the specifics of the influence of a face protective mask on the change in psychophysiological qualities of a car driver as a factor in safety eventuality under urban traffic conditions.The method of an open-ended survey of 108 healthy adult drivers was used to obtain a quantitative subjective assessment of the effect of face masks on changing driving safety conditions and a comfortable emotional state while driving. A qualitative analysis of assessment of the level of psychophysiological qualities of drivers wearing and not wearing a face mask was carried out using Meleti hardware-software complex.A sharp decrease in neuropsychic functions with a simultaneous increase in quality of thinking and visual analysis of the traffic situation was revealed regarding the drivers wearing a face protective mask compared to those driving without a mask while the level of psychomotor reaction remains unchanged regardless of the gender of the driver.The subjective assessment of survey participants of the effect of a face mask on professionally important, psychophysiological characteristics of drivers revealed a significant (41,7 %) or insignificant (20,4 %) decrease in reaction, while 38 % of drivers did not notice significant changes in driving because of the effect of the mask.Based on these results, it is assumed that the face mask may serve as a predictor of a road pre-accident situation.To assess the effect of the face mask on the driver, a coefficient of eventuality of reducing road safety is proposed. It is recommended to use it as an additional factor in a situational pandemic environment when developing recommendations for the use of face masks for car and bus drivers, and when analysing the causes of road accidents.

Influence of Landscape Design on Driving Behavior Based on Slope Illusion

In a continuous downhill section of a mountain highway, factors such as road alignment, roadside environment, and other visual characteristics will impact the slope illusion drivers experience and engage in unsafe driving behaviors. To improve the negative consequences of slope illusion and driving safety in continuous downhill sections, the effects of plant spacing, height, roadside distance, and color on driving behavior were all studied by simulating the plant landscape in a virtual environment. A driving simulator and UC-win/road software were used to conduct an indoor driving simulation experiment, and parameters such as speed and lateral position offset were used as the evaluation indices of driving stability to reflect the driver’s speed perception ability with subjective equivalent speeds. The results show that a plant landscape with appropriate plant spacing, height, roadside separation, and color is conducive to improving driving stability. Furthermore, a landscape with a height of 3 m, spacing of 10 m, roadside spacing of 0.75 m, and appropriate color matching can enhance the slope perception ability and speed perception ability of drivers, which is conducive to improving the driving safety of continuous downhill sections.

Mental workLoad Accumulation Effect of Mobile Phone Distraction in L2 Autopilot Mode

As self-driving vehicles become more common, there is a need for precise measurement and definition of when and in what ways a driver can use a mobile phone in autonomous driving mode, for how long it can be used, the complexity of the call content, and the accumulated psychological load. This study uses a 2 (driving mode) * 2 (call content complexity) * 6 (driving phase) three-factor mixed experimental design to investigate the effect of these factors on the driver's psychological load by measuring the driver's performance on peripheral visual detection tasks, pupil diameter, and EEG components in various brain regions in the alpha band. The results showed that drivers' mental load levels converge between manual and automatic driving modes as the duration of driving increases, regardless of the level of complexity of the mobile phone conversation. This suggests that mobile phone conversations can also disrupt the driver's cognitive resource balance in automatic driving mode, as it increases mental load while also impairing the normal functioning of brain functions such as cognitive control, problem solving, and judgment, thereby compromising driving safety.

Stochastic model predictive control for driver assistance control of intelligent vehicles considering uncertain driving environment

Advanced driver assistance control faces great challenges in cooperating with the nearby vehicles. The assistance controller of an intelligent vehicle has to provide control efforts properly to prevent possible collisions without interfering with the drivers. This paper proposes a novel driver assistance control method for intelligent ground vehicles to cooperate with the nearby vehicles, using the stochastic model predictive control algorithm. The assistance controller is designed to correct the drivers’ steering maneuvers when there is a risk of possible collisions, so that the drivers are not interfered. To enhance the cooperation between the vehicles, the nearby vehicle motion is predicted and included in the assistance controller design. The position uncertainties of the nearby vehicle are considered by the stochastic model predictive control approach via chance constraints. Simulation studies are conducted to validate the proposed control method. The results show that the assistance controller can help the drivers avoid possible collisions with the nearby vehicles and the driving safety can be guaranteed.

Characterization of Counter-Surface Substrates for a Laboratory Abrasion Tester (LAT100) Compared with Asphalt and Concrete to Predict Car Tire Performance

Tire performance is determined based on the interaction between the tire and the road as a counter-surface, and is of the utmost importance for driving safety. When studying tire friction and abrasion, the characteristics of the roads/counter-surfaces are crucial. The excitations on the tire come from the road asperities. A proper characterization of the counter-surface texture is, therefore, an absolute necessity in order to optimize tire performance. The present study provides the required knowledge over the counter-surfaces employed as common substrates in a Laboratory Abrasion Tester (LAT100), which are typically based on embedded corundum particles for dry/wet friction and abrasion experiments. All surfaces are scanned and characterized by laser microscopy. The surface micro and macro roughness/textures are evaluated and compared with asphalt and concrete as the real roads by power spectral densities (PSD). The reliability of the high-frequency data based on the device type should be considered carefully. The reliable cut-off wavenumber of the PSDs is investigated based on image analyses on the range of tested frequency for micro and macro textures obtained by optical scanning devices. The influence of the texture wavelength range on the rubber−surface interaction is studied on a laboratory scale.

A Nonintrusive and Real-Time Classification Method for Driver’s Gaze Region Using an RGB Camera

Driver distraction has become a leading cause of traffic crashes. Visual distraction has the most direct impact on driving safety among various driver distractions. If the driver’s line of sight deviates from the road in front, there will be a high probability of visual distraction. A nonintrusive and real-time classification method for driver’s gaze region is proposed. A Multi-Task Convolutional Neural Network (MTCNN) face detector is used to collect the driver’s face image, and the driver’s gaze direction can be detected with a full-face appearance-based gaze estimation method. The driver’s gaze region is classified by the model trained through the machine learning algorithms such as Support Vector Machines (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN). The simulated experiment and the real vehicle experiment were conducted to test the method. The results show that it has good performance on gaze region classification and strong robustness to complex environments. The models in this paper are all lightweight networks, which can meet the accuracy and speed requirements for the tasks. The method can be a good help for further exploring the visual distraction state level and exert an influence on the research of driving behavior.

Proposal for Preventing Motor Vehicle Accident Related to Drowsy Driving in Patients With Narcolepsy

Narcolepsy is a chronic sleep disorder characterized by irresistible sleep attacks, hypersomnolence, cataplexy (sudden loss of muscle tone provoked by emotion), and sleep paralysis. Individuals with narcolepsy are at a high risk of experiencing sleepiness while driving leading to road traffic accidents. To prevent such accidents, some countries have regulations for commercial and noncommercial drivers with narcolepsy. Evaluating sleepiness is essential. Therefore, several subjective reports and objective tests were used to predict the possibility of car crashes or near-misses. Brain stimulants are effective in treating narcolepsy and can reduce daytime sleepiness in these patients. However, no guideline has been established for the driving safety of patients with narcolepsy in Korea. The Korean Sleep Research Society has prepared this proposal for preventing motor vehicle accidents caused by drowsy driving in patients with narcolepsy.