A Unified Approach to Driver Assistance Systems Based on Artificial Potential Fields

1999 ◽  
Vol 123 (3) ◽  
pp. 431-438 ◽  
Author(s):  
J. Christian Gerdes ◽  
Eric J. Rossetter
Keyword(s):  
Control Systems ◽  
Potential Fields ◽  
Driver Assistance ◽  
Unified Approach ◽  
Driver Assistance Systems ◽  
Control Approach ◽  
Artificial Potential Fields ◽  
Individual Potential ◽  
Assistance Systems ◽  
Safe Set

This paper presents an approach to vehicle control based on the paradigm of artificial potential fields. Using this method, the dynamics of the vehicle are coupled with the environment in a manner that ensures that the system exhibits safe motion in the absence of driver inputs. The driver remains in control of the vehicle, however, with the control systems presenting a predictable and safe set of dynamics. With the control approach presented here, integration of various assistance systems is easily achieved through simple superposition of individual potential and damping functions. A simple example of a combined lanekeeping and stability system demonstrates how this can be accomplished. Preliminary simulation results suggest that both safety and driveability are achievable with such a system, prompting further investigation.

A Unified Approach to Driver Assistance Systems Based on Artificial Potential Fields

1999 ◽  
Author(s):  
J. Christian Gerdes ◽  
Eric J. Rossetter
Keyword(s):  
Control Systems ◽  
Potential Fields ◽  
Driver Assistance ◽  
Unified Approach ◽  
Driver Assistance Systems ◽  
Control Approach ◽  
Artificial Potential Fields ◽  
Individual Potential ◽  
Assistance Systems ◽  
Safe Set

Abstract This paper presents an approach to vehicle control based upon the paradigm of artificial potential fields. Using this method, the dynamics of the vehicle are coupled with the environment in a manner that ensures that the system exhibits safe motion in the absence of driver inputs. The driver remains in control of the vehicle, however, with the control systems presenting a predictable and safe set of dynamics. With the control approach presented here, integration of various assistance systems can be easily achieved through simple superposition of individual potential and damping functions. A simple example of a combined lanekeeping and stability system demonstrates how this can be accomplished. Preliminary simulation results suggest that both safety and driveability are achievable with such a system, prompting further investigation.

scholarly journals Rare Traffic Signs Recognition

2021 ◽  
Author(s):  
Tingir Badmaev ◽  
Vlad Shakhuro ◽  
Anton Konushin
Keyword(s):  
Control Systems ◽  
Autonomous Vehicles ◽  
Synthetic Data ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Traffic Signs ◽  
Road Signs ◽  
Automatic Mode ◽  
Assistance Systems ◽  
Rare Classes

Recognition of road signs is an important part of the control systems of autonomous vehicles and driver assistance systems. Modern recognition methods based on neural networks require large well-labeled datasets. Marking up data is quite expensive, but it is even more difficult to mark up rare classes of objects. To solve this problem in this article, we use synthetic data. We improve the marking of the Russian traffic signs dataset (RTSD) in semi-automatic mode adding 9 thousand new road signs. We perform an experimental evaluation of the currently best classifiers and detectors in the task of recognizing road signs. To improve the performance of classification, we use stochastic weight averaging (SWA) and contrastive loss. The use of modern methods allows us to train a high-quality neural network on synthetic data, which was previously impossible, and significantly improves the metrics of recognition of both rare and frequent road signs.

scholarly journals Machine Learning Based Framework for Vehicle Make and Model Recognition

2020 ◽  
Vol 9 (1) ◽  
pp. 1639-1643
Keyword(s):  
Machine Learning ◽  
Access Control ◽  
Control Systems ◽  
Traffic Monitoring ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Main Road ◽  
Assistance Systems ◽  
Toll Collection ◽  
Model Recognition

Nowadays, the speedy increase with in the rage of the automobiles on the main road and urban roads have created several challenges regarding the proper management and management of traffic. This is a very significant framework for intelligent traffic monitoring and management. Vehicle analysis is an important component for many smart applications, that includes automated toll collection, self-guided car driver assistance systems, smart car parking systems. In a recent years, due to increased security awareness in parking lots, restricted areas and building for access control systems, the need to identify and classify the vehicles has become important.

scholarly journals Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021 ◽  
Vol 13 (8) ◽  
pp. 4264
Author(s):  
Matúš Šucha ◽  
Ralf Risser ◽  
Kristýna Honzíčková
Keyword(s):  
Autonomous Vehicles ◽  
Qualitative Data ◽  
Expert Interviews ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistant Systems ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Technical Solutions ◽  
Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

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