Advanced Driver Assistance Systems Systems Research Articles

Automated system of recognition of road signs for ADAS systems

Advanced Driver Assistance Systems (ADAS) require an environmental recognition function to inform the driver and for making management decisions. To solve the problems of pattern recognition, it is necessary to use one of the image classification methods. To date, there are quite a few similar algorithms that differ in the quality and speed of recognition. This paper presents a comparative analysis of classification algorithms in the problems of road signs recognition based on the GTSRB dataset. The results of the work showed that the most promising methods of classification in the problems of image recognition are the methods of reference vectors and k – nearest neighbors.

Software framework for runtime application monitoring of fail-safe multi-processor ADAS SoCs

In advanced driver assistance systems (ADAS), it is very critical to ensure a system malfunction does not cause harm to humans (also referred to as functional safety). Malfunctions could occur due to incorrect software (SW) execution. Many techniques exist to catch systematic SW faults during software and application development. However, without run-time monitoring of key parameters, it is impossible to guarantee a fail-safe system. In this paper, we describe a software framework to monitor application statistics in heterogeneous multiprocessor SoCs targeted for fail-safe ADAS systems. With < 0.5% CPU load overhead per monitor, the proposed framework was found to accurately report statistics for a front camera analytics application.

A Logic Architecture for 360 ADAS-Alerts for Hazards Detection Based in Driver Actions

In this work is presented a novel approach for passive safety in vehicles by Advanced Driver Assistance Systems (ADAS) alert emission in 360° around driver to notify about hazards nearby the vehicle depending on the actions taken by driver per the context. This proposal would create a more robust system compared to current passive ADAS systems since the feedback to driver is in the same direction that hazard is detected (Punctual Sound Source Alert), compared with most assistance systems that emits sounds from the monitor or from the dashboard provoking distractions when emits alerts unnecessarily. The increase of security by this method will allow the driver to be aware of their surroundings even in a very quiet cabin or in a noisy environment. Also, it would detect the steering wheel angle, speed of movement and the activation of turning lights among other alerts, which would allow us to define a critical action during driving; apart from using sensors and cameras aimed at the driver to detect patterns of movement during these critical actions and have a prediction of a possible turn or manoeuvre when driving, refer to Figure 1. It will be necessary a reconfiguration of the alert in frequency, time of action depending upon the level of risk to prevent an accident or to reduce the consequences in an imminent accident.

Development of an Eco-Cruise Control System Based on Digital Topographical Data

Rapid growth in economies and a sharp increase in the present quantity of vehicles have contributed to congested cities, traffic accidents, energy crises and air pollution, which not only cause serious economic damage but also affect human life and safety. According to the Kyoto Protocol international standards and consensus, the trend in the automotive industry is to develop more ecological vehicles in order to reduce fuel consumption and air pollution. Recently, a number of modern premium cars have been equipped with advanced driver assistance systems (ADAS), especially an adaptive cruise control (ACC) system, which was found to be one of the most widely and successful ADAS systems to date. However, the road topography on highways affects fuel consumption of vehicles. Rapid acceleration, rapid deceleration, and unreasonable gear use can all result in increasing fuel consumption. Therefore, an eco-cruise control (ECC) system on roads with up-down slopes is critical for improving vehicle fuel economy in coordination with the Global Positioning System (GPS) and Geographic Information System (GIS). This proposed approach combines the road elevation profile of digital road maps with nonlinear optimal predictive control (NOPC) algorithms to handle the vehicle velocity control; it is aimed at providing the most economical speed according to road grade. The developed algorithms not only provide ecological driving guidance for the driver, but also contribute to driving safety. Finally, extensive simulations demonstrate that the proposed controller can significantly reduce fuel consumption of a vehicle while ensuring driving comfort and safety.

A Multimodal ADAS System for Unmarked Urban Scenarios Based on Road Context Understanding

Comprehensive situational awareness is paramount to the effectiveness of advanced driver assistance systems (ADASs) used in daily urban traffic, particularly for the unmarked roads, which cannot fulfill the requirements of conventional ADAS systems. This paper proposed a stereovision-based multimodal ADAS system designed for expanding the usability of ADAS functions, including lane-keeping assist, adaptive cruise control, and precrash system, to normal urban scenarios with unmarked roads. At first, the physical road boundary and vehicle candidates are detected. Subsequently, the contextual information between the host vehicle, the road, and the other vehicles are correlated for both low-level object detection improvement and high-level road structure estimation. Finally, the required ADAS elements are generated based on the correlation results with respect to the system functionalities. Experimental results in various typical but challenging scenarios have substantiated the effectiveness of the proposed system, which could help increase the value of the existing ADAS system without major modifications or expense.

A Reliable Method for Detecting Road Regions from a Single Image Based on Color Distribution and Vanishing Point Location

Abstract Numerous advanced driver assistance systems (ADAS) are gaining popularity even in the mid to low end segment cars. Vision based technologies assisted by the use of cameras cater to a lot of these ADAS systems. These systems enhance the safety of the driver, passenger and pedestrians on the road. In a typical image taken from an on-board camera of a car, it is the road region that occupies most of the pixels. Therefore, an approach for detecting and eliminating road regions from an optical image is proposed which in turn speeds up computations for further object/ROI detection. Our proposed road detection algorithm works in two stages: i) vanishing point detection; and ii) road region identification. Experimental results show that this approach performs better with real-road images of varying texture, colour and shapes.

Eye Gesture Analysis with Head Movement for Advanced Driver Assistance Systems

Road traffic accidents are a major cause of death worldwide. In an attempt to reduce accidents, some research efforts have focused on creating Advanced Driver Assistance Systems (ADAS) able to detect vehicle, driver and environmental conditions and to use this information to identify cues for potential accidents. This paper presents continued work on a novel Non-intrusive Intelligent Driver Assistance and Safety System (Ni-DASS) for assessing driver point of regard within vehicles. It uses an onboard CCD camera to observe the driver’s face. A template matching approach is used to compare the driver’s eye-gaze pattern with a set of eye-gesture templates of the driver looking at different focal points within the vehicle. The windscreen is divided into cells and comparison of the driver’s eye-gaze pattern with templates of a driver’s eyes looking at each cell is used to determine the driver’s point of regard on the windscreen. Results indicate that the proposed technique could be useful in situations where low resolution estimates of driver point of regard are adequate. For instance, To allow ADAS systems to alert the driver if he/she has positively failed to observe a hazard.