Ontology-Based Pervasive Spatial Knowledge for Car Driver Assistance

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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Design and Implementation of Driver Assistance System using Machine Learning

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/994/1/012006 ◽

2020 ◽

Vol 994 ◽

pp. 012006

Author(s):

D. S. Bhargava ◽

N. Shyam ◽

K. Senthil Kumar ◽

M. Wasim Raja ◽

P Sivashankar.

Keyword(s):

Machine Learning ◽

Driver Assistance System ◽

Assistance System ◽

Driver Assistance ◽

Design And Implementation

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Car2Car Communication Using a Modulated Backscatter and Automotive FMCW Radar

Sensors ◽

10.3390/s21113656 ◽

2021 ◽

Vol 21 (11) ◽

pp. 3656

Author(s):

Antonio Lazaro ◽

Marc Lazaro ◽

Ramon Villarino ◽

David Girbau ◽

Pedro de Paco

Keyword(s):

Power Supply ◽

Communication Systems ◽

Microwave Frequency ◽

Proof Of Concept ◽

Driver Assistance ◽

Switching Rate ◽

Fmcw Radar ◽

Time Signals ◽

Video Sensors ◽

24 Ghz

This work proposes the use of a modulated tag for direct communication between two vehicles using as a carrier the wave emitted by an FMCW radar installed in the vehicle for advanced driver assistance. The system allows for real-time signals detection and classification, such as stop signal, turn signals and emergency lights, adding redundancy to computer video sensors and without incorporating additional communication systems. A proof-of-concept tag has been designed at the microwave frequency of 24 GHz, consisting of an amplifier connected between receiving and transmitting antennas. The modulation is performed by switching the power supply of the amplifier. The tag is installed on the rear of the car and it answers when it is illuminated by the radar by modulating the backscattered field. The information is encoded in the modulation switching rate used. Simulated and experimental results are given showing the feasibility of the proposed solution.

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