scholarly journals Towards Road Safety in LMICs: Vehicles that Guide Drivers on Self-Explaining Roads

2020 ◽  
Author(s):  
Hans Godthelp
Keyword(s):  
Road Safety ◽  
System Approach ◽  
Heavy Traffic ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Middle Income ◽  
Road Design ◽  
Design Characteristics ◽  
Self Driving Cars ◽  
The Way

Abstract: Traffic collisions cause a huge problem of public health in low and middle income countries.. The safe system approach is generally considered as the leading concept on the way to road safety. Based on the fundamental premise that humans make mistakes, the overall traffic system should be ‘forgiving’. Sustainable safe road design is one of the key elements of the safe system approach. However, the road design principles behind the safe system approach are certainly not leading in today’s infrastructure developments in most LMICs. Cities are getting larger and road networks are expanding. In many cases, existing through-roads in local communities are up-graded, resulting in heavy traffic loads and high speeds on places, that are absolutely not suited for this kind of through-traffic. Furthermore a safe system would require that functional design properties of cars and roads would be conceptually integrated, which is not the case at all. Although advanced driver assistance systems are on their way of development for quite a long period, their potential role in the safe system concept is mostly unclear and at least strongly underexposed. The vision on future cars is dominated by the concept of automation. This paper argues that the way to self-driving cars, should take a route via the concept of guidance, i.e. vehicles that guide drivers, both on self-explaining roads and on more or less unsafe roads. Such an in-vehicle support system may help drivers to choose transport mode, route and speed, based on criteria related to safety and sustainability. It is suggested to develop a driver assistance system using relatively simple and cheap technologies, particularly for the purpose of use in LMICs. Such a GUIDE (Generic User Interface for Driving Evolution) may make roads self-explaining - not only by their physical design characteristics - but also by providing in-car guidance for drivers. In future the functional characteristics of both cars and roads should be conceptualized into one integrated safe system, in which the user plays the central role. As such GUIDE may serve as the conceptual bridge between vehicle and roadway characteristics. It is argued that GUIDE is necessary to bring a breakthrough in road safety developments in LMICs and also give an acceleration towards zero fatalities in HICs.

Advanced driver assistance systems: Using multimodal redundant warnings to enhance road safety

2017 ◽  
Vol 58 ◽  
pp. 238-244 ◽  
Author(s):  
Francesco Biondi ◽  
David L. Strayer ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi ◽  
Claudio Mulatti
Keyword(s):  
Road Safety ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Assistance Systems

scholarly journals The choice of rational complex of active safety systems for smart cars

2021 ◽  
Vol 2061 (1) ◽  
pp. 012128
Author(s):  
A I Markovnina ◽  
N D Tsyganov ◽  
A V Papunin ◽  
V S Makarov ◽  
V V Belyakov
Keyword(s):  
Road Safety ◽  
Vehicle Safety ◽  
Active Safety ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
The Road ◽  
Active Safety Systems ◽  
Assistance Systems ◽  
Smart Cars ◽  
Safety Systems

Abstract The problem of ensuring road safety affects all elements of the Driver-Car-Road-Environment system. Smart cars equipped with enough traffic assistants can significantly improve road safety. Active vehicle safety systems, including intelligent driver assistance systems and assistants, perform similar road safety functions. With all the variety of possibilities for equipping cars with systems complexes, the need arises to assess the feasibility and profitability of installing a particular complex of systems. For this, it is proposed to apply the methods of multi-criteria assessment. As a result of calculations, the best options for the sets of systems that most widely cover the road situation have been identified.

scholarly journals Assessment of road safety parameters in the city of Kigali, Rwanda

2021 ◽  
Vol 27 (2) ◽  
pp. 209-219
Author(s):  
David Nkurunziza ◽  
Rahman Tafahomi ◽  
Irumva Augustin Faraja
Keyword(s):  
Road Safety ◽  
Geometric Parameters ◽  
Design Standards ◽  
Middle Income ◽  
Road Crashes ◽  
Road Design ◽  
Sight Distance ◽  
Average Variance ◽  
Safety Parameters ◽  
The City

Increase in vehicular population has led to increase in road crashes. This is particularly very evident in low- and middle-income countries. Rwanda is no exception to this problem. While various factors influence the occurrence of a crash, it is argued that road design can make roads either safe or unsafe to drive. This research examines geometric parameters of roads in the City of Kigali, with emphasis on checking their safety parameters in comparison with design standards of AASHTO 2011. The case study of this research was „KN 123 St‟, a two-lane asphalt road located in the center of the City of Kigali. Road parameters like lane widths, curve radii, super-elevation, sight distances and slope grades were examined. The research found various areas of improvement, inconsistencies and non-conformities. The findings established a clear relationship between ignored safety parameters during design and construction, and road crashes that happened on specifically identified hazardous spots. For instance, there is an extreme abrupt change in lane widths over the whole length at a rate of 74%. Unsafe sharp curves make half of all evaluated horizontal curves. Curves with the smallest radii have already recorded many crashes. The study found that super-elevation values have been inadequately computed, designed, and constructed with an average variance of 5%. About 80% of assessed vertical curves had insufficient stopping sight distance and 90% of headlight sight distance likewise. Apart from geometric parameters, high operating speeds of car drivers and motorcyclists, lack of shoulders, lack of zebra crossings and left sidewalk were found as extra causes of traffic injuries. While widening of the road could potentially help meet most safety parameters, it is arguably expensive and unrealistic. Therefore, this study recommends speed governance, forgiving roadside features, traffic signalization, and road markings as tools to alert drivers where most crash-prone areas are. Keywords: Road safety, Geometric design, Safety assessment, Road Crashes, AASHTO 2011

scholarly journals Explaining Deep Learning-Based Driver Models

2021 ◽  
Vol 11 (8) ◽  
pp. 3321
Author(s):  
Maria Paz Sesmero Lorente ◽  
Elena Magán Lopez ◽  
Laura Alvarez Florez ◽  
Agapito Ledezma Espino ◽  
José Antonio Iglesias Martínez ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Research Area ◽  
Key Factors ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Level Of Automation ◽  
Critical Areas ◽  
Self Driving Cars

Different systems based on Artificial Intelligence (AI) techniques are currently used in relevant areas such as healthcare, cybersecurity, natural language processing, and self-driving cars. However, many of these systems are developed with “black box” AI, which makes it difficult to explain how they work. For this reason, explainability and interpretability are key factors that need to be taken into consideration in the development of AI systems in critical areas. In addition, different contexts produce different explainability needs which must be met. Against this background, Explainable Artificial Intelligence (XAI) appears to be able to address and solve this situation. In the field of automated driving, XAI is particularly needed because the level of automation is constantly increasing according to the development of AI techniques. For this reason, the field of XAI in the context of automated driving is of particular interest. In this paper, we propose the use of an explainable intelligence technique in the understanding of some of the tasks involved in the development of advanced driver-assistance systems (ADAS). Since ADAS assist drivers in driving functions, it is essential to know the reason for the decisions taken. In addition, trusted AI is the cornerstone of the confidence needed in this research area. Thus, due to the complexity and the different variables that are part of the decision-making process, this paper focuses on two specific tasks in this area: the detection of emotions and the distractions of drivers. The results obtained are promising and show the capacity of the explainable artificial techniques in the different tasks of the proposed environments.

scholarly journals The effectiveness of the AEB system in the context of the safety of vulnerable road users

2021 ◽  
Vol 11 (1) ◽  
pp. 977-993
Author(s):  
Monika Ucińska ◽  
Małgorzata Pełka
Keyword(s):  
Road Safety ◽  
Road Accident ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Emergency Braking ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Assistance Systems ◽  
Potential Impact ◽  
National Police

Abstract According to the analysis by the National Police Headquarters, roughly 40% of all road accident victims in Poland are vulnerable road users (VRU), i.e. pedestrians and cyclists. Their protection has become one of the priorities for action regarding road safety. For this purpose, various activities are carried out aimed not only at human behaviour or the development of modern and safe road infrastructures but also at the development of modern vehicles, including advanced driver assistance systems (ADAS). In order to identify the limitations of the currently available driver assistance systems, designed to respond to VRU, research was carried out under the project name, “PEDICRASH: Safety aspects of VRU in CAD automated vehicles.” The project was aimed at increasing users’ awareness (both pedestrians and drivers) of the limitations of ADAS by analysing barriers and indicating recommendations allowing for more effective protection of pedestrians and cyclists due to the identified operating limitations of these systems. The research focused on the autonomous emergency braking (AEB) system and its potential impact on the level of road safety, with particular emphasis on VRU.

scholarly journals Experimental Validation on Intersection Turning Trajectory Prediction Method for Advanced Driver Assistance System Based on Triclothoidal Curve

2021 ◽  
Vol 11 (13) ◽  
pp. 5900
Author(s):  
Yohei Fujinami ◽  
Pongsathorn Raksincharoensak ◽  
Shunsaku Arita ◽  
Rei Kato
Keyword(s):  
Traffic Accidents ◽  
Computational Cost ◽  
Prediction Method ◽  
Smooth Curve ◽  
Assistance System ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Trajectory Prediction ◽  
Left Turn ◽  
Left Hand

Advanced driver assistance systems (ADAS) for crash avoidance, when making a right-turn in left-hand traffic or left-turn in right-hand traffic, are expected to further reduce the number of traffic accidents caused by automobiles. Accurate future trajectory prediction of an ego vehicle for risk prediction is important to activate the assistance system correctly. Our objectives are to propose a trajectory prediction method for ADAS for safe intersection turnings and to evaluate the effectiveness of the proposed prediction method. Our proposed curve generation method is capable of generating a smooth curve without discontinuities in the curvature. By incorporating the curve generation method into the vehicle trajectory prediction, the proposed method could simulate the actual driving path of human drivers at a low computational cost. The curve would be required to define positions, angles, and curvatures at its initial and terminal points. Driving experiments conducted at real city traffic intersections proved that the proposed method could predict the trajectory with a high degree of accuracy for various shapes and sizes of the intersections. This paper also describes a method to determine the terminal conditions of the curve generation method from intersection features. We set a hypothesis where the conditions can be defined individually from intersection geometry. From the hypothesis, a formula to determine the parameter was derived empirically from the driving experiments. Public road driving experiments indicated that the parameters for the trajectory prediction could be appropriately estimated by the obtained empirical formula.

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