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.

Bosch's approach toward automated driving

2015 ◽  
Vol 63 (3) ◽  
Author(s):  
Jan Becker ◽  
Sören Kammel ◽  
Oliver Pink ◽  
Michael Fausten
Keyword(s):  
Driver Assistance ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Level Of Automation ◽  
Assistance Systems ◽  
Fully Automated Driving

AbstractAdvanced driver assistance systems already help drivers reach their destinations safely and more comfortably. Future systems will evolve from driver assistance over highly automated vehicles to fully automated driving. With an increasing level of automation, automated functions will reduce the driver's burden more and more, thereby creating space for productivity, communication or entertainment while driving. In this article we outline our roadmap for future automated vehicles, assess the key challenges for introduction and give an overview of the major algorithmic components.

scholarly journals User Education in Automated Driving: Owner’s Manual and Interactive Tutorial Support Mental Model Formation and Human-Automation Interaction

Information ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 143 ◽  
Author(s):  
Yannick Forster ◽  
Sebastian Hergeth ◽  
Frederik Naujoks ◽  
Josef Krems ◽  
Andreas Keinath
Keyword(s):  
Conceptual Development ◽  
Method Development ◽  
Driving Simulator ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Automation Systems ◽  
Interactive Tutorial ◽  
Model Formation ◽  
User Education

Automated driving systems (ADS) and a combination of these with advanced driver assistance systems (ADAS) will soon be available to a large consumer population. Apart from testing automated driving features and human–machine interfaces (HMI), the development and evaluation of training for interacting with driving automation has been largely neglected. The present work outlines the conceptual development of two possible approaches of user education which are the owner’s manual and an interactive tutorial. These approaches are investigated by comparing them to a baseline consisting of generic information about the system function. Using a between-subjects design, N = 24 participants complete one training prior to interacting with the ADS HMI in a driving simulator. Results show that both the owner’s manual and an interactive tutorial led to an increased understanding of driving automation systems as well as an increased interaction performance. This work contributes to method development for the evaluation of ADS by proposing two alternative approaches of user education and their implications for both application in realistic settings and HMI testing.

scholarly journals Auditory interfaces in automated driving: an international survey

2015 ◽  
Author(s):  
Pavlo Bazilinskyy ◽  
Joost C. F. De Winter
Keyword(s):  
Warning System ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Sound System ◽  
Collision Warning ◽  
English Speaking ◽  
Assistance Systems ◽  
Forward Collision Warning ◽  
Auditory Interfaces

This study investigated peoples’ opinion on auditory interfaces in contemporary cars and their willingness to be exposed to auditory feedback in automated driving. We used an Internet-based survey to collect 1,205 responses from 91 countries. The participants stated their attitudes towards two existing auditory driver assistance systems, a parking assistant (PA) and forward collision warning system (FCWS), as well as towards a futuristic augmented sound system (FS) proposed for fully automated driving. The respondents were positive towards the PA and FCWS, and rated their willingness to have these systems as 3.87 and 3.77, respectively (1 = disagree strongly, 5 = agree strongly). The respondents tolerated the FS. The results showed that a female voice is the most preferred feedback mode for the support of takeover requests in highly automated driving, regardless of whether the respondents’ country is English speaking or not. The present results could be useful for designers of automated vehicles and other stakeholders.

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.

Adaptive intervention logic for automated driving systems based on injury risk minimization

2020 ◽  
Vol 234 (13) ◽  
pp. 2975-2987
Author(s):  
Dario Vangi ◽  
Antonio Virga ◽  
Michelangelo-Santo Gulino
Keyword(s):  
Injury Risk ◽  
Simulation Software ◽  
Motor Vehicles ◽  
Adaptive Logic ◽  
Driver Assistance ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Adaptive Intervention ◽  
Assistance Systems

Performance improvement of advanced driver assistance systems yields two major benefits: increasingly rapid progress towards autonomous driving and a simultaneous advance in vehicle safety. Integration of multiple advanced driver assistance systems leads to the so-called automated driving system, which can intervene jointly on braking and steering to avert impending crashes. Nevertheless, obstacles such as stationary vehicles and buildings can interpose between the opponent vehicles and the working field of advanced driver assistance systems’ sensors, potentially resulting in an inevitable collision state. Currently available devices cannot properly handle an inevitable collision state, because its occurrence is not subject to evaluations by the system. In the present work, criteria for intervention on braking and steering are introduced, based on the vehicle occupants’ injury risk. The system must monitor the surrounding and act on the degrees of freedom adapting to the evolution of the scenario, following an adaptive logic. The model-in-the-loop, software-in-the-loop and hardware-in-the-loop for such adaptive intervention are first introduced. To highlight the potential benefits offered by the adaptive advanced driver assistance systems, simulation software has been developed. The adaptive logic has been tested in correspondence of three inevitable collision state conditions between two motor vehicles: at each instant, the adaptive logic attitude of creating impact configurations associated with minimum injury risk is ultimately demonstrated.

scholarly journals Checklist for Expert Evaluation of HMIs of Automated Vehicles—Discussions on Its Value and Adaptions of the Method within an Expert Workshop

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 233 ◽  
Author(s):  
Nadja Schömig ◽  
Katharina Wiedemann ◽  
Sebastian Hergeth ◽  
Yannick Forster ◽  
Jeffrey Muttart ◽  
...  
Keyword(s):  
Evaluation Methods ◽  
Assessment Method ◽  
Evaluation Methodology ◽  
Santa Fe ◽  
Driver Assistance ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Driver Assistance Systems ◽  
Hands On ◽  
Assistance Systems

Within a workshop on evaluation methods for automated vehicles (AVs) at the Driving Assessment 2019 symposium in Santa Fe; New Mexico, a heuristic evaluation methodology that aims at supporting the development of human–machine interfaces (HMIs) for AVs was presented. The goal of the workshop was to bring together members of the human factors community to discuss the method and to further promote the development of HMI guidelines and assessment methods for the design of HMIs of automated driving systems (ADSs). The workshop included hands-on experience of rented series production partially automated vehicles, the application of the heuristic assessment method using a checklist, and intensive discussions about possible revisions of the checklist and the method itself. The aim of the paper is to summarize the results of the workshop, which will be used to further improve the checklist method and make the process available to the scientific community. The participants all had previous experience in HMI design of driver assistance systems, as well as development and evaluation methods. They brought valuable ideas into the discussion with regard to the overall value of the tool against the background of the intended application, concrete improvements of the checklist (e.g., categorization of items; checklist items that are currently perceived as missing or redundant in the checklist), when in the design process the tool should be applied, and improvements for the usability of the checklist.

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