Intelligent Headlight Research Articles

Semi-Autonomous Smart Electric Vehicle with AI based Drive Assistance

In our research, we propose a practical methodology design and implementation of a semi-autonomous smart electric vehicle (EV) equipped with advanced features such as AI-based pothole detection, a robust battery management system (BMS), self-parking capability, and intelligent headlight intensity control. The primary objective of this research is to enhance the safety, efficiency, and user experience of electric vehicles through the integration of cutting-edge technologies.The AI-based pothole detection system utilizes sensors and machine learning algorithms to continuously monitor the road surface for hazards such as potholes, cracks, or debris. By analyzing real-time sensor data, the system can accurately identify potential road hazards and provide timely warnings to the driver or take proactive measures to mitigate their impact on vehicle performance and passenger comfort. The battery management system plays a critical role in optimizing the performance and longevity of the vehicle’s battery pack. Through advanced algorithms and predictive analytics, the BMS monitors various parameters such as temperature, voltage, and current to ensure safe and efficient operation of the battery. By dynamically adjusting charging rates and load distribution, the system maximizes the battery’s lifespan while ensuring sufficient energy availability for propulsion and onboard systems. Self-parking technology enables the vehicle to autonomously navigate parking spaces and execute precise maneuvers without human intervention.

A review of automotive intelligent and adaptive headlight beams intensity control approaches

The automotive headlight stands out as a critical vehicle component, particularly emphasized during nighttime driving. The high beam, designed for optimal driver visibility on long-distance roads, traditionally relies on manual control by the driver. However, this manual control poses challenges, particularly when the high beam light temporarily blinds oncoming drivers. The resultant dazzle for drivers of opposing vehicles is a significant concern. In response to these issues, there is a growing demand for adaptive and intelligent headlights that can autonomously adjust beam intensity. The intelligent headlight system takes on the responsibility of modifying the beam intensities without requiring explicit input from the drivers. This study aims to systematically review various approaches to controlling intelligent headlight beam intensity. The paper identifies four prominent approaches to intelligent headlight beam intensity control, recognized as widely used techniques. Furthermore, the study uncovers intriguing connections between some of these intensity control approaches. A survey on utilization rates indicates that sensor-based and machine learning (ML)-based intensity control approaches are the most commonly employed methods by automotive headlight designers. The paper concludes by providing insights into the future prospects of intelligent headlight technology, offering guidance for future researchers in this field.

Analysis of Effective Flicker Light Patterns to Improve the Pedestrian Detectability from a Driver for an Intelligent Headlight System

Analysis of Effective Flicker Light Patterns to Improve the Pedestrian Detectability from a Driver for an Intelligent Headlight System

Laser Phosphor Light Source with Hot Spot for Intelligent Headlight using DMD for Ultra-High Beam Applications

Laser Phosphor Light Source with Hot Spot for Intelligent Headlight using DMD for Ultra-High Beam Applications

Laser Phosphor Light Source with Hot Spot for Intelligent Headlight using DMD for Ultra-High Beam Applications

Laser Phosphor Light Source with Hot Spot for Intelligent Headlight using DMD for Ultra-High Beam Applications

Efficient local adaptive thresholding for night-time vehicle candidate detection

In the current commercial automotive market, the need for intelligent headlight control systems has increased more and more. Camera-based night-time vehicle detection has become a crucial issue in determining the performance of such control systems. The purpose of this paper is to offer an answer to the question, 'Which thresholding method is suitable in terms of detection performance for a night-time vehicle candidate selection process?' For such purposes, two local adaptive thresholding methods are introduced and tested. One is local maximum-based thresholding, and the other is local mean-based thresholding. Efficient implementation methodologies are also introduced for real-time processing. Through the simulations tested on road image sequences with different exposure times, we prove that local adaptive thresholding methods have better performance than other well-known global thresholding methods. In particular, the simulations show that the proposed mean-based thresholding method has better performance on both long- and short-exposure sequences.

Hyperspectral Image-Based Night-Time Vehicle Light Detection Using Spectral Normalization and Distance Mapper for Intelligent Headlight Control.

This paper proposes a vehicle light detection method using a hyperspectral camera instead of a Charge-Coupled Device (CCD) or Complementary metal-Oxide-Semiconductor (CMOS) camera for adaptive car headlamp control. To apply Intelligent Headlight Control (IHC), the vehicle headlights need to be detected. Headlights are comprised from a variety of lighting sources, such as Light Emitting Diodes (LEDs), High-intensity discharge (HID), and halogen lamps. In addition, rear lamps are made of LED and halogen lamp. This paper refers to the recent research in IHC. Some problems exist in the detection of headlights, such as erroneous detection of street lights or sign lights and the reflection plate of ego-car from CCD or CMOS images. To solve these problems, this study uses hyperspectral images because they have hundreds of bands and provide more information than a CCD or CMOS camera. Recent methods to detect headlights used the Spectral Angle Mapper (SAM), Spectral Correlation Mapper (SCM), and Euclidean Distance Mapper (EDM). The experimental results highlight the feasibility of the proposed method in three types of lights (LED, HID, and halogen).

Embedded Based Smart, Secured and Safe Vehicle System

In today's fast and crowded world, road accident is a major concern. The highest fatal traffic accidents occur on the curved roads at night time. Also the glare problem due to focus of headlight is dangerous. Even the improper indications given to the nearby vehicle increases chances of fatal accidents. These facts indicate the importance of advancement in conventional vehicle systems. This paper explains the prototype model of vehicle system consisting of intelligent headlight intensity control, adaptive headlights, auto indicator-off system, anti-collision system and vehicle to vehicle communication. Proposed system is very cost efficient and reliable and can be mounted on any vehicle, it is specially focused to increase the safety and security of low-end vehicles.

The Research of CAN Bus in the Car Intelligent Lighting Control System

As one of the most important subsystem of automotive control systems, the design of automotive lighting control system is essential. In this article, the automotive intelligent headlight control system based on CAN bus was introduced, and the system hardware structure and software design processes was given. Adopted MC9S08DZ60 microcontroller which integrated the CAN controller as the master chip. While taking advantage of the photoelectric sensor to analyze road lighting conditions, according to light changes in the external environment to achieve automatic dimmer. The circuit has characteristics of simple hardware design, high reliability and real-time. And provided a theoretical basis for the Hyundai Motor intelligent control system.

Enhancing Light Blob Detection for Intelligent Headlight Control Using Lane Detection

In this paper, we propose an enhanced method for detecting light blobs (LBs) for intelligent headlight control (IHC). The main function of the IHC system is to automatically convert high-beam headlights to low beam when vehicles are found in the vicinity. Thus, to implement the IHC, it is necessary to detect preceding or oncoming vehicles. Generally, this process of detecting vehicles is done by detecting LBs in the images. Previous works regarding LB detection can largely be categorized into two approaches by the image type they use: low-exposure (LE) images or autoexposure (AE) images. While they each have their own strengths and weaknesses, the proposed method combines them by integrating the use of the partial region of the AE image confined by the lane detection information and the LE image. Consequently, the proposed method detects headlights at various distances and taillights at close distances using LE images while handling taillights at distant locations by exploiting the confined AE images. This approach enhances the performance of detecting the distant LBs while maintaining low false detections.

Interference filters for innovative night vision systems

Even though night-time driving accounts for only 20 % of all traffic, around 40 % of all fatal accidents occur at night. In order to prevent these accidents, car manufacturers and their suppliers are developing new, intelligent headlight technology. Innovative night vision systems provide the driver with additional information on the road ahead, especially in the more distant areas in front of the vehicle, thus helping to avoid collisions. Schott AG has developed special interference filters to series production readiness for use in these night vision systems. This article describes the design, development and manufacturing of these filters.