In autonomous and self-driving news are ROHM, research and Baraja.
ROHM High Powers Laser Diode
ROHM has developed a high-power laser diode – the RLD90QZW8. It is ideal for industrial equipment and consumer applications requiring distance measurement and spatial recognition.
In recent years, LiDAR is being increasingly adopted in a wide range of applications that require automation – including AGVs (Automated Guided Vehicles), robot vacuums, and autonomous vehicles – where it is necessary to accurately measure distance and recognize space. In this context, there is a need to improve the performance and output of laser diodes when used as light sources to increase detection distance and accuracy.
To meet this demand, ROHM established original patented technology to achieve a narrower emission width that contributes to longer range and higher accuracy in LiDAR applications. In 2019, ROHM released a 25W laser diode RLD90QZW5 followed by a 75W laser diode RLD90QZW3 in 2021. In response to the growing market demand for even higher output, ROHM developed a new 120W laser diode.
The RLD90QZW8 is a 120W infrared high output laser diode developed for LiDAR used in distance measurement and spatial recognition in 3D ToF systems. Original device development technology allows ROHM to reduce the temperature dependence of the laser wavelength by 66% over general products, to just ⊿11.6nm (Ave. 0.10nm/°C). This makes it possible to narrow the bandpass filter while extending the detection range of LiDAR. At the same time, a uniform light intensity of 97% is achieved over the industry’s smallest class* of emission width of 270µm, representing a range of 264µm that contributes to higher resolution. Additional features that include high power-to-light conversion efficiency (PCE) enables efficient optical output that contributes to lower power consumption in LiDAR applications.
A variety of design support materials necessary for integrating and evaluating the new product is available free of charge on ROHM’s website that facilitate market introduction. In order to drive laser diodes with high nano-second order speed required for LiDAR applications, ROHM developed a reference design available now that combines ROHM’s 150V EcoGaN™ HEMT and gate drivers.
ROHM has also acquired certification under the IATF 16949 automotive quality management standard for both front-end and back-end processes at its manufacturing facilities. As a result, product development of laser diodes for automotive applications (AEC-Q102 compliant) is underway, with commercialization planned by the end of 2024.
New Autonomous Research
Autonomous vehicles hold the promise of tackling traffic congestion, enhancing traffic flow through vehicle-to-vehicle communication, and revolutionizing the travel experience by offering comfortable and safe journeys. Additionally, integrating autonomous driving technology into electric vehicles could contribute to more eco-friendly transportation solutions.
A critical requirement for the success of autonomous vehicles is their ability to detect and navigate around obstacles, pedestrians, and other vehicles across diverse environments. Current autonomous vehicles employ smart sensors such as LiDARs (Light Detection and Ranging) for a 3D view of the surroundings and depth information, RADaR (Radio Detection and Ranging) for detecting objects at night and cloudy weather, and a set of cameras for providing RGB images and a 360-degree view, collectively forming a comprehensive dataset known as point cloud. However, these sensors often face challenges like reduced detection capabilities in adverse weather, on unstructured roads, or due to occlusion.
To overcome these shortcomings, an international team of researchers led by Professor Gwanggil Jeon from the Department of Embedded Systems Engineering at Incheon National University (INU), Korea, has recently developed a groundbreaking Internet-of-Things-enabled deep learning-based end-to-end 3D object detection system. “Our proposed system operates in real time, enhancing the object detection capabilities of autonomous vehicles, making navigation through traffic smoother and safer,” explains Prof. Jeon. Their paper was made available online on October 17, 2023, and published in Volume 24, Issue 11 of the journal IEEE Transactions on Intelligent Transport Systems on November 2023.
The proposed innovative system is built on the YOLOv3 (You Only Look Once) deep learning object detection technique, which is the most active state-of-the-art technique available for 2D visual detection. The researchers first used this new model for 2D object detection and then modified the YOLOv3 technique to detect 3D objects. Using both point cloud data and RGB images as input, the system generates bounding boxes with confidence scores and labels for visible obstacles as output.
To assess the system’s performance, the team conducted experiments using the Lyft dataset, which consisted of road information captured from 20 autonomous vehicles traveling a predetermined route in Palo Alto, California, over a four-month period. The results demonstrated that YOLOv3 exhibits high accuracy, surpassing other state-of-the-art architectures. Notably, the overall accuracy for 2D and 3D object detection were an impressive 96% and 97%, respectively.
Prof. Jeon emphasizes the potential impact of this enhanced detection capability: “By improving detection capabilities, this system could propel autonomous vehicles into the mainstream. The introduction of autonomous vehicles has the potential to transform the transportation and logistics industry, offering economic benefits through reduced dependence on human drivers and the introduction of more efficient transportation methods.”
Furthermore, the present work is expected to drive research and development in various technological fields such as sensors, robotics, and artificial intelligence. Going ahead, the team aims to explore additional deep learning algorithms for 3D object detection, recognizing the current focus on 2D image development.
In summary, this groundbreaking study could pave the way for a widespread adoption of autonomous vehicles and, in turn, a more environment-friendly and comfortable mode of transport.
Baraja A-Samples of Doppler RMCW Spectrum-Scan LiDAR
Baraja, creator of the breakthrough Spectrum-Scan™ LiDAR technology, announces the availability of A-Samples of all the integrated components necessary to build a new-to-world Doppler RMCW Spectrum-Scan™ LiDAR for automotive integration.
A LiDAR has two main jobs to perform: measure distance (ranging) and steering a light (scanning), and it’s a complex system that integrates electronics, optics, photonics, advanced software algorithms for signal processing with the rigor of industrial quality and automotive cost and scale.
“Multiple heterogeneous sub-systems are contained in a LiDAR, this would require each sub-system’s integration via completely different classes of chips and using different processes. Baraja announces today that all the different chips and integrated components (Tx, Rx, Amplification, Processing and Steering) to build the highest performance LiDAR offering, including Doppler velocity, are now available as A-Samples for integration” said Federico Collarte, Baraja CEO, “we’ve made it really simple to plug-and-play the components to get the best performance LiDAR and the beauty of the solution is that the technologies developed to get to these chips self-reinforce and make possible further integration in the future”.
Light amplification – the SOA
The Semiconductor Optical Amplifier (SOA) integrates the amplifier chip, together with a custom-developed high-efficiency Thermo-electric cooler into a hermetically sealed and automotive-grade package and it’s able to amplify the laser light to achieve the >200m range requirements, while being mass-produceable (chip process) and 50-80x smaller and lower power consumption compared to fiber amplifiers used by legacy 1550nm LiDAR, including Baraja’s own Off-Road first generation product.
Light emission and reception – the BOSA
The Bidirectional Optical Sub-Assembly (BOSA) integrates 3 types of chips in an automotive-grade, hermetically sealed and temperature controlled enclosure (3×3.5cm). These chips are: the laser chip (InP), the homodyne receiver chip (silicon photonics) and the TIA chips (silicon). These, plus micro optics and automotive-grade Thermo-electric coolers deliver an end-to-end solution to produce and receive the wavelength-tuneable laser light that is the hallmark of Spectrum-Scan™, so in a sense the BOSA also partially integrates the “steering” job of the LiDAR.
Development process
Developing optical sub-assemblies and “chips”, if done well is about a 36-month process. “I’m pleased to share that now with our A-Samples available, we’re right on track to launching Spectrum HD 2025 C-samples/General Availability in 2025, and if you want to have access to this truly transformational technology it’s a great time to work together” said Cibby Pulikkaseril, Baraja CTO.