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Abstract
A new scheme of LiDAR-embedded smart laser headlight module (LHM) for autonomous vehicles is proposed and demonstrated. The LiDAR sensor was fabricated by LeddarTech with the wavelength of 905-nm, whereas the LHM was fabricated by a highly reliable glass phosphor material that exhibited excellent thermal stability. The LHM consisted of two blue laser diodes, two blue LEDs, a yellow glass phosphor-converter layer with a copper thermal dissipation substrate, and a parabolic reflector to reflect the blue light and the yellow phosphor light combined into white light. The LHM exhibited a total output optical power of 9.5 W, a luminous flux of 4,000 lm, a relative color temperature of 4,300 K, and an efficiency of 421 lm/W. The high-beam patterns of the LHMs were measured to be 180,000 luminous intensity (cd) at 0° (center), 84,000 cd at ± 2.5°, and 29,600 cd at ± 5°, which met the ECE R112 class B regulation. The low-beam patterns also satisfied the ECE R112 class B regulation as well. Integrating the signals received from the Lidar detection and CCD image by a smart algorithm, we demonstrated the generation of smart on/off signals for controlling the laser headlights. The recognition rate of the objects was evaluated to be more than 86%. This novel LiDAR-embedded smart LHM with the unique highly reliable glass phosphor-converter layer is favorable as one of the most promising candidates for use in the next-generation high-performance autonomous vehicle applications.
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
1. Introduction
LiDAR stands for light imaging, detection and ranging. LiDAR has seen extensive use in autonomous vehicles, robotics, aerial mapping, and atmospheric measurements. LiDAR is one of key components for autonomous vehicles. LiDAR sensors emit infrared laser lights to scan and detect objects in the near or far vicinity of the sensors and create a 3D map of the surroundings environment [1–3]. In automotive applications of the LiDAR technology, most of the existing LiDAR sensors are mounted on the top of the vehicle. LiDAR sensors continuously rotate and generate thousands of laser pulses per second. These high-speed pulsed laser beams from the LiDAR are continuously emitted in the 360-degree surroundings of the vehicle and are reflected by the objects. Employing smart algorithms, the signal received through LiDAR scanner is converted into real-time 3D graphical data, which are often displayed as 3D maps of the surrounding objects. However, placing the LiDAR sensor on the top of the vehicle may cause many issues, such as close-range dead angular zones, collecting dust, water corrosion, and difficulty in connecting the electrical system of the LiDAR sensors. In addition, this position of the LiDAR does not appeal to the aesthetic requirements of the customers. In contrast to the LiDAR sensors mounted on the top of the vehicle, integrating the LiDAR sensor into the headlight system could provide a solution to solve the aforementioned issues. Therefore, the close-range dead angular zones and air/water corrosion of the LiDAR are prevented by the cover of headlight. The electrical system and heat-dissipation can easily handle.
The primary benefit for drivers using laser headlights is that the operating range can be up to 600 m [4]. This offers the driver improved visibility, and contributes significantly to road traffic safety. However, the eye safety is an important issue using laser headlights since high power blue lasers are being used. Therefore, it is important to install a monitor sensor in the blue laser system. If there is a functional failure caused by an unavoidable accident, the sensor will sense the problems and send a signal to turn off the blue lasers, preventing the risk of laser leakage.
In this study, we propose and demonstrate a new scheme of smart laser headlights module (LHM) with embedded LiDAR sensor by integrating the optical system of the LiDAR into the headlight as a single unit, in which the laser headlight was controlled by feedbacks generated from the Lidar sensor. The LiDAR sensor used was manufactured by the LeddarTech [5]. The LHM was fabricated by packaging two blue laser diodes, two blue LEDs, a yellow glass phosphor-converter layer with a copper thermal dissipation substrate, and one parabolic reflector to reflect blue light and yellow phosphor light combined to produce white light output [6–12]. The yellow glass phosphor-converter layers were fabricated by a low-temperature of 750°C, which exhibited excellent thermal stability [6–8]. The measured high-beam and low-beam patterns of the LHMs met the specifications of the ECE R112 (Economic Commission Europe R112) class B regulation. Employing a smart algorithm through integration of the signals from LiDAR detection and CCD image, we demonstrated the generation of smart on/off signals for controlling the laser headlights. The recognition rate of vehicles and objects was evaluated to be more than 86%. This proposed novel LiDAR embedded smart LHM with highly reliable glass phosphor-converter layer is favorable as one of the most promising candidates for use in the next-generation high-performance autonomous vehicle applications.
2. Fabrication of glass-based phosphor-converter layer
The primary benefit for drivers using laser diode (LD) headlights is that the operating range can be up to 600-m [4]. This offers the driver improved visibility, contributing significantly to road traffic safety. Most of the white LD engines are integrated using blue LD and phosphor-converter layer. The laser headlights based phosphor-converted layers had been fabricated using ceramic [13], single crystal [14], and glass materials [15]. However, the fabrication temperatures of the ceramic- and single crystal-based phosphor were over 1200°C and 1500°C, respectively. These high-temperature fabrication requirements had been difficult for the commercial production. In previous reports [6–8], the glass-based phosphor-converter layers made by processes with temperature as low as 750°C had shown to have better thermal stability than that of the silicone-based color conversion layers. The glass-based phosphor with better thermal stability is one of the most promising materials for use in the LD light engines.
The fabrication procedures of glass-based yellow phosphor-converter layer (Ce3+: YAG) started with the preparation of sodium mother glass by melting the mixture of the raw materials at 1300°C and dispersing Ce3+: YAG powders into the mixture by gas-pressure and sintering under different temperatures [6–8]. The composition of the sodium mother glass was 60 mol% SiO2, 25 mol% Na2CO3, 9 mol% Al2O3, and 6 mol% CaO. The resultant cullet glass of the SiO2-Na2CO3-Al2O3-CaO were dried and milled into powders. The Ce3+:YAG crystals were then uniformly mixed with the mother glass and sintered at 750°C for 1 hour and annealed at 350°C for 3 hours, followed by cooling to room temperature. The concentration of Ce3+:YAG with 40 wt% exhibited higher luminous efficiency and provided better purity for the yellow phosphor-converter layers [6–8]. The glass phosphor bulk was then cut into disks of phosphor-converter layer with a diameter of 100 mm and thickness with 0.2 mm.
In comparison with the commercial silicone-based phosphor-converter layers (CeYDS), the glass-based phosphor-converter layers (CeYDG) exhibited better thermal stability in lumen degradation and lower chromaticity shift. The lumen losses of CeYDS were about 10 times higher than that of CeYDG at 250°C after thermal aging for 1008 hours [8]. The chromaticity shifts of CeYDS were about 40 times larger than that of CeYDG at 250°C, after thermal aging for 1008 hours [8]. These were due to that the glass-based phosphor-converter layers exhibited higher transition temperature (550°C), smaller thermal expansion coefficient (9 ppm/°C), higher thermal conductivity (1.38W/m°C), and higher Young’s modulus (70 GPa) than the silicone-based phosphor-converter layers [6–8].
3. Design and fabrication of high-beam laser headlight module (LHM) and low-beam LED headlight module (LEDHM)
3.1. Design and measurement of high-beam laser headlight module (LHM)
Figure 1 shows an integrated smart laser headlight with Lidar, which includes of a high-beam laser headlight module (LHM), a low-beam LED headlight module (LEDHM), and a LiDAR module. The high-beam LHM consisted of two blue laser diodes, two blue LEDs, a yellow glass phosphor-converter layer with a copper thermal dissipation substrate, and one parabolic reflector to reflect blue light and yellow phosphor light combined into white light output, as shown in Fig. 2. A reflow solder technique for mounting the glass converter on the copper substrate was used. There were three steps in the reflow solder method. (1) An oxygen free copper layer was electroplated on the copper substrate. (2) The yellow glass phosphor converter layer was attached with SAC305 (Sn: 3 wt.% and Ag: 0.5 wt.%) onto the copper substrate. (3) The copper substrate was pre-heated at 160°C for 1 min and then re-heated at 260°C for 30s to complete the glass converter layer on the copper substrate.
Nichia blue lasers with wavelength of 445-nm were used in this work. The LHM exhibited a total output optical power of 9.5 W, a luminous flux of 4000 lm, a relative color temperature of 4,300 K, and an efficiency of 420 lm/W. The yellow glass phosphor-converter layer was fabricated by a low-temperature process of 750°C and mounted on a copper thermal dissipation substrate. The infrared thermal imaging camera showed that the temperature profile of the LHM with copper substrate was an average temperature of 48°C after an operation time of more than one hour. The copper thermal dissipation substrate solved the thermal effect of the LHM. A flat-refractor was used to integrate the two blue laser beams together and was reflected into the glass phosphor-converter layer. The parabolic-reflector improved the white light pattern of the LHM to satisfy the ECE R112.
Simulation software, SPEOS was used to design the high-beam LHM. Figure 3 shows the ray tracing diagram and distribution pattern of the high-beam LHM. In this study, the eye safety is an important issue since high power lasers are used. A sensor, as indicated by a blue rectangle below the white light source, as shown in Fig. 2, will be installed to monitor the proper function of the glass phosphor layer. If there is functional failure caused by an unavoidable accident, the sensor will sense these problems and send a signal to turn off the blue lasers, preventing the risk of laser leakage. The high-beam patterns of the LHMs were measured and simulated, as shown in Table 1. The high-beam patterns of the LHM were measured to be 180,000 luminous intensity (cd) at 0°(center), 84,000 cd at ± 2.5°, and 29,600 cd at ± 5°, which were well satisfied the ECE R112 class B regulation. The difference between the measurement and simulation of the patterns could be caused by fabrication and assembly error. The operating range of high-beam headlight was measured more than 300-m.
Table 1. Measurement, simulation, and the specification of ECE R112 class B for high beam LHM.
3.2. Design and measurement of low-beam LED headlight module (LEDHM)
Figure 4(a) shows a schematic diagram of low-beam LED headlight module (LEDHM). This LEDHM consisted of five blue LEDs, an elliptical-reflector, a mask, an aspherical lens, and glass phosphor-converter layers on copper substrate, as shown in Fig. 4(b). OSRAM blue LEDs with wavelength of 445-nm were used in this work. The LEDHM exhibited a luminous flux of 3100 lm, a correlated temperature of 6,000 K, and an efficiency of 310 lm/W. Figure 5 shows the simulation of ray tracing results and the 2D intensity distribution pattern of LED low-beam module, which was based on the design of each test point and asymmetric cut-off line with a mask. In the low-beam headlight of the vehicle with the driver on the left-hand side, an asymmetric cut-off line was necessary to illuminate distance and significantly prevent the amounts of light from being cast into the eyes of drivers of the oncoming vehicles, as indicated in Fig. 5(b). Cut-off line was established on as a natural part separating bright and dark area in the conventional low beam. It was also the essential function in the visual aiming of the headlights. The cut-off line definition was a horizontal straight line on the side opposite to the direction of traffic in which the headlight was intended. The shape of cut-off line was horizontal on the left side and slanted at 15° to the right or angled at 45° degree to the horizontal, as shown in Fig. 5(b).
Fig. 4. (a) Schematic diagram of low-beam LEDM, (b) cross section of glass phosphor-converter layers.
Fig. 5. Simulation of (a) ray tracing diagram and (b) 2D intensity distribution pattern for LED low-beam module.
The low-beam patterns of the LEDHMs were measured and simulated, as shown in Table 2 in which all of the test points were well followed the low-beam of the ECE R112. The low-beam patterns of the LEDHM were measured to be 44,800 luminous intensity (cd) at Zone I, 448 cd at Zone III, and 3,158 cd at Zone IV, which were well satisfied the low-beam of the ECE R112 class B regulation. The difference between the measurement and simulation of the patterns could be caused by fabrication and assembly error.
Table 2. Measurement, safety accreditation of ECE R112 class B, and simulation for low-beam LED module.
4. Package and measurement of LiDAR sensor
A LiDAR module (LeddarVu8-Medium) [5] embedded in smart laser headlight module (LHM) with the LiDAR detection software is shown in Fig. 6. With the feedback of the LiDAR, a smart LHM can control the headlight field, avoiding high-reflection areas at night and pay attention to all directions to ensure safe driving. The LeddarVu8-Medium, as shown in Fig. 6(a), was used to track multiple objects simultaneously within the field of view of the sensor, including lateral discrimination without any moving parts, which was embedded in the laser headlight, as shown in Fig. 6(b). The light source of LiDAR (low part of Fig. 6(a)) was a 905-nm laser emitter combined with diffractive optics that provided a wide illumination beam with viewing angle of 48° (horizontal) × 3° (vertical). The distance of the object was determined by time-of-flight method with the pulse signal at wavelength of 905-nm. The optical power of laser was 2-W and operating range for the LiDAR was 40-m. The receiver assembly (up part of Fig. 6(a)) included eight independent detection elements with simultaneous multi-object measurement capabilities supported by software with signal processing algorithms, as shown in Fig. 6(c).
Fig. 6. (a) LeddarVu8-Medium, (b) embedded LiDAR module in smart laser headlight (c) software of signal processing algorithm.
The LiDAR sensor had 8 channels at 48 degrees, which respectively output 8 detected object distances, and the 8 channels correspond to the high-beam area. The detected multi-objects were shown in the green lines at 40-m. Using optical path and wavelength differences, the optical signal of LiDAR did not interfere with laser headlight. Therefore, high quality optical data could be obtained. The smart chips and software technology integrated the LiDAR sensor signals and CCD image signals to determine the distances of different objects from a large amount of data, which provided fast feedback to ensure safe driving.
5. Recognition of smart LHM
5.1. Hue saturation value method
In this study, a simple method using the Hue Saturation Value (HSV) to determine detection and tracking robustness of the vehicle is proposed. The HSV method can describe colors in terms of their shade and brightness. Employing HSV method, the recognition rate of vehicle and the brightness/shade area used to control the headlight are determined. This provides the driver with a better view and significantly improves road traffic safety. Figure 7 shows a block diagram of the proposed HSV method. The HSV method consisted of RGB to HSV space, HSV filter, morphological image processing, image labeling function, block size limit, region of interest (ROI) area with frame and center cross, LiDAR data input, and the illumination area. All these together determine how the headlights are controlled for illumination. The color of the headlights can be roughly divided into white and yellow. Two upper and lower thresholds of HSV were set by using two HSV filters, allowing the use of only the headlights and taillights.
5.2. Headlight illumination control
In this work, we are only interested in the CCD visual area where the headlights are illuminated, in which the CCD images are integrated with the LiDAR data into the image recognition board [16]. We define a 6 × 2 region of interest (ROI) in the headlight illuminated area according to the range of driver visibility reducing the computational complexity and the possibility of misjudgment. When the lights entered the ROI area, the position of the vehicles was marked with the blue squares and blue crosses in the image area through the recognition software, as shown in Fig. 8(a). For the case 2 in Fig. 8(b), we assumed that a pedestrian and the lights entered the ROI area, the position of a pedestrian and lights were marked with a blue square (CCD image), a red square (LiDAR data), and distances (LiDAR data), which the ROI area was marked by the recognition software. According to the design of the smart laser headlight, when the vehicles and pedestrians enter the ROI areas, the smart laser headlight will be turned off at these areas. After the vehicles and pedestrians leave the ROI area, the smart laser headlight will be turned on again. To demonstrate the cases where the sensor misses the objects in the ROI areas or produces positive signal without any objects, the video sequences have been manually labelled. The video resolution was 960 × 540 when testing was conducted. The detection algorithm was evaluated by measuring bounding box intersection between annotation and the bounding box obtained by grouping detection. If the intersection percent was more than 70%, the detection was proclaimed as valid. The experimental results showed the correct detections of 702, missed detections of 97, and false positives of 31. Therefore, the detection rate was evaluated to be as 86%. The combination of the LiDAR sensor signals and CCD image signals resulted in a higher certainty in object detection compared to using the CDD image signals alone.
6. Discussion and conclusion
Recently, most of the existing LiDAR sensors have been installed on the top of the vehicle in automotive applications. However, placing the LiDAR sensor on the top of the vehicle may cause many issues, such as close-range dead angular zones, collecting dust, water corrosion, and difficulty in connecting to the electrical system of the LiDAR sensors. In this study, in contrast to the LiDAR sensors mounted on the top of the vehicle, integrating the LiDAR sensor into the headlight system could provide a solution to solve the aforementioned issues. Furthermore, the electrical system and heat-dissipation of the LiDAR sensor can easily hand.
In summary, a new scheme of LiDAR embedded smart laser headlight module (LHM) for autonomous vehicles was proposed and demonstrated. The LHM was fabricated by a unique glass phosphor-converter layer, which exhibited excellent thermal stability. The results measured high-beam and low-beam patterns of the LHMs were well satisfied the ECE R112 class B regulation. Employing a smart algorithm through the integration of the LiDAR sensor signals and CCD image signals, the generation of smart on/off signals for controlling the laser headlights was demonstrated. The recognition rate of the objects was evaluated to be more than 86%. Further study of recognition rate with improvement up to more than 95% may be developed. One of the approaches is to use the fusion of multiple-sensors with advanced AI algorithm. Such sensors include LiDAR detection with point-cloud techniques, short distance radars, and more high resolution CCD cameras. This proposed novel LiDAR embedded smart LHMs with the innovative highly reliable glass phosphor is favorable as one of the most promising candidates for use in the next-generation high-performance autonomous vehicle applications.
Funding
Ministry of Science and Technology, Taiwan (MOST) (107-2218-E-005-025).
Acknowledgments
The authors would like to thank the higher education sprout project on the ministry of education at Taiwan (R.O.C.).
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