On Driver Behavior Recognition for Increased Safety: A Roadmap

Abstract

Advanced Driver-Assistance Systems (ADASs) are used for increasing safety in the automotive domain, yet current ADASs notably operate without taking into account drivers’ states, e.g., whether she/he is emotionally apt to drive. In this paper, we first review the state-of-the-art of emotional and cognitive analysis for ADAS: we consider psychological models, the sensors needed for capturing physiological signals, and the typical algorithms used for human emotion classification. Our investigation highlights a lack of advanced Driver Monitoring Systems (DMSs) for ADASs, which could increase driving quality and security for both drivers and passengers. We then provide our view on a novel perception architecture for driver monitoring, built around the concept of Driver Complex State (DCS). DCS relies on multiple non-obtrusive sensors and Artificial Intelligence (AI) for uncovering the driver state and uses it to implement innovative Human–Machine Interface (HMI) functionalities. This concept will be implemented and validated in the recently EU-funded NextPerception project, which is briefly introduced.

Harnessing Communication Heterogeneity: Architectural Design, Analytical Modeling, and Performance Evaluation of an IoT Multi-Interface Gateway

“…In this case, similarly to scenarios where UAVs should interact with each other, on the ground (i.e., along roads and highways) there may be the need to collect data (e.g., for monitoring purposes) directly from the vehicle and the driver by relying on the MIG. This would involve (i) the interaction of the MIG with the vehicle's On Board Unit (OBU) for diagnostic information retrieval, through wireless or wired communication interfaces, as well as (ii) data collection at the MIG from additional equipment and devices installed inside the vehicle's cabin (e.g., driver's smartphone and wearables for monitoring his/her stress levels [30]), through wireless communication interfaces.…”

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

“…Using an advanced driver assistance system for increasing safety was proposed in [26]. Driving security and quality for both passengers and drivers are ensured in this paper.…”

Development of a Safety Heavy-Duty Vehicle Model Considering Unsafe Acts, Unsafe Conditions and Near-Miss Events Using Structural Equation Model

“…The study conducts literature reviews on the four dimensions by gathering and analyzing the documents as listed in Table 1 [11][12][13][14][15][16][17][18][19][20]. The literature reviews have found that a driver is influenced by the behaviors of other drivers under complex traffic situations, time pressure or at-risk events as they are causes of frustration in driving [21]. Regarding the driver characteristics that can be extracted from their behaviors [22], the researchers analyze the Driver Behavior Questionnaire on the correlation and risk of accidents on a tollway [23][24][25][26][27][28][29] from the variables such as abnormality, mistakes and rule violations [30].…”