scholarly journals Driving-Induced Neurological Biomarkers in an Advanced Driver-Assistance System

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6985
Author(s):  
Iqram Hussain ◽  
Seo Young ◽  
Se-Jin Park
Keyword(s):  
Resting State ◽  
Driver Assistance System ◽  
Driving Simulator ◽  
Road Traffic ◽  
Mental Workload ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Multi Class Classification ◽  
Prediction Approach

Physiological signals are immediate and sensitive to neurological changes resulting from the mental workload induced by various driving environments and are considered a quantifying tool for understanding the association between neurological outcomes and driving cognitive workloads. Neurological assessment, outside of a highly-equipped clinical setting, requires an ambulatory electroencephalography (EEG) headset. This study aimed to quantify neurological biomarkers during a resting state and two different scenarios of driving states in a virtual driving environment. We investigated the neurological responses of seventeen healthy male drivers. EEG data were measured in an initial resting state, city-roadways driving state, and expressway driving state using a portable EEG headset in a driving simulator. During the experiment, the participants drove while experiencing cognitive workloads due to various driving environments, such as road traffic conditions, lane changes of surrounding vehicles, the speed limit, etc. The power of the beta and gamma bands decreased, and the power of the delta waves, theta, and frontal theta asymmetry increased in the driving state relative to the resting state. Delta-alpha ratio (DAR) and delta-theta ratio (DTR) showed a strong correlation with a resting state, city-roadways driving state, and expressway driving state. Binary machine-learning (ML) classification models showed a near-perfect accuracy between the resting state and driving state. Moderate classification performances were observed between the resting state, city-roadways state, and expressway state in multi-class classification. An EEG-based neurological state prediction approach may be utilized in an advanced driver-assistance system (ADAS).

scholarly journals Advanced Driver-Assistance System (ADAS) for Intelligent Transportation Based on the Recognition of Traffic Cones

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Liyong Wang ◽  
Peng Sun ◽  
Min Xie ◽  
Shaobo Ma ◽  
Boxiong Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Machine Vision ◽  
Driver Assistance System ◽  
Vision System ◽  
Intelligent Transportation ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Depth Information ◽  
Driver Assistance ◽  
Temporary Road

Great changes have taken place in automation and machine vision technology in recent years. Meanwhile, the demands for driving safety, efficiency, and intelligence have also increased significantly. More and more attention has been paid to the research on advanced driver-assistance system (ADAS) as one of the most important functions in intelligent transportation. Compared with traditional transportation, ADAS is superior in ensuring passenger safety, optimizing path planning, and improving driving control, especially in an autopilot mode. However, level 3 and above of the autopilot are still unavailable due to the complexity of traffic situations, for example, detection of a temporary road created by traffic cones. In this paper, an analysis of traffic-cone detection is conducted to assist with path planning under special traffic conditions. A special machine vision system with two monochrome cameras and two color cameras was used to recognize the color and position of the traffic cones. The result indicates that this novel method could recognize the red, blue, and yellow traffic cones with 85%, 100%, and 100% success rate, respectively, while maintaining 90% accuracy in traffic-cone distance sensing. Additionally, a successful autopilot road experiment was conducted, proving that combining color and depth information for recognition of temporary road conditions is a promising development for intelligent transportation of the future.

scholarly journals Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System

2020 ◽  
Vol 10 (8) ◽  
pp. 2645 ◽  
Author(s):  
Changwoo Park ◽  
Seunghwan Chung ◽  
Hyeongcheol Lee
Keyword(s):  
Real World ◽  
Driver Assistance System ◽  
Test Method ◽  
Assistance System ◽  
Advanced Driver Assistance System ◽  
Driver Assistance ◽  
Maintenance Time ◽  
Synchronization Module ◽  
Vehicle Controllers ◽  
Test Scenarios

Most vehicle controllers are developed and verified with V-model. There are several traditional methods in the automotive industry called “X-in-the-Loop (XIL)”. However, the validation of advanced driver assistance system (ADAS) controllers is more complicated and needs more environmental resources because the controller interacts with the external environment of the vehicle. Vehicle-in-the-Loop (VIL) is a recently being developed approach for simulating ADAS vehicles that ensures the safety of critical test scenarios in real-world testing using virtual environments. This new test method needs both properties of traditional computer simulations and real-world vehicle tests. This paper presents a Vehicle-in-the-Loop topology for execution in global Coordinates system. Also, it has a modular structure with four parts: synchronization module, virtual environment, sensor emulator and visualizer, so each part can be developed and modified separately in combination with other parts. This structure of VIL is expected to save maintenance time and cost. This paper shows its acceptability by testing ADAS on both a real and the VIL system.

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