Nowadays, and more than a decade after the first steps towards autonomous driving, we keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being a key instrument for the success of this technology. Such advances trigger the emergence of new players in the automotive industry, and along with car manufacturers, this sector represents a multibillion-dollar market where everyone wants to take a share. To understand recent advances and technologies behind LiDAR, this article presents a survey on LiDAR sensors for the automotive industry. With this work, we show the measurement principles and imaging techniques currently being used, going through a review of commercial systems and development solutions available in the market today. Furthermore, we highlight the current and future challenges, providing insights on how both research and industry can step towards better LiDAR solutions. 

Automotive LiDAR Technology: A Survey

Optical phased array (OPA) technology is considered a promising solution for solid-state beam steering to supersede the traditional mechanical beam steering. As a key component of the LIDAR system for long-range detection, OPAs featuring a wide steering angle and high resolution without beam aliasing are highly desired. However, a wide steering range requires a waveguide pitch less than half of the wavelength, which is easily subjected to cross talk. Besides, high resolution requires a large aperture, and it is normally achieved by a high count number of waveguides, which complicates the control system. To solve the mentioned issues, we design two high-performance 128-channel OPAs fabricated on a multilayered SiN-on-SOI platform. Attributed to the nonuniform antenna pitch, only 128 waveguides are used to achieve a 4 mm wide aperture. Besides, by virtue of innovative dual-level silicon nitride (Si3N4) waveguide grating antennas, the fishbone antenna OPA achieves a 100°×19.4° field of view (FOV) with divergence of 0.021°×0.029°, and the chain antenna OPA realizes a 140°×19.23° FOV with divergence of 0.021°×0.1°. To our best knowledge, 140° is the widest lateral steering range in two-dimensional OPA, and 0.029° is the smallest longitudinal divergence. Finally, we embed the OPA into a frequency-modulated continuous-wave system to achieve 100 m distance measurement. The reflected signal from 100 m distance is well detected with 26 dBm input transmitter power, which proves that OPA serves as a promising candidate for transceiving optical signal in a LIDAR system.

Wide-steering-angle high-resolution optical phased array

The global Connected and Autonomous Mobility industry is growing at a rapid pace. To ensure the successful adoption of connected automated mobility solutions, their safety, reliability and hence the public acceptance are paramount. It is widely known that in order to demonstrate that L3+ automated systems are safer with respect to human drivers, upwards of several millions of miles need to be driven. The only way to efficiently achieve this amount of tests in a timely manner is by using simulations and high fidelity virtual environments. Two key components of being able to test an automated system in a synthetic environment are validated sensor models and noise models for each sensor technology. In fact, the sensors are the element feeding information into the system in order to enable it to safely plan the trajectory and navigate. In this paper, we propose an innovative real-time LiDAR sensor model based on beam propagation and a probabilistic rain model, taking into account raindrop distribution and size. The model can seamlessly run in real-time, synchronised with the visual rendering, in immersive driving simulators, such as the WMG 3xD simulator. The models are developed using Unreal engine, therefore demonstrating that gaming technology can be merged with the Automated Vehicles (AVs) simulation toolchain for the creation and visualization of high fidelity scenarios and for AV accurate testing. This work can be extended to add more sensors and more noise factors or cyberattacks in real-time simulations.

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Realistic LiDAR With Noise Model for Real-Time Testing of Automated Vehicles in a Virtual Environment

The optical power handling of an OPA scanning beam determines its targeted detection distance. So far, a limited number of investigations have been conducted on the restriction of the beam power. To the best of our knowledge, we for the first time in this paper explore the ability of the silicon photonics based OPA circuit for the high power application. A 64-channel SiN-Si based one-dimensional (1D) OPA chip has been designed to handle high beam power to achieve large scanning range. The chip was fabricated on the standard silicon photonics platform. The main lobe power of our chip can reach 720 mW and its peak side-lobe level (PSLL) is -10.33 dB. We obtain a wide scanning range of 110° in the horizontal direction at 1550 nm wavelength, with a compressed longitudinal divergence angle of each scanning beam of 0.02°.

Investigation and demonstration of a high-power handling and large-range steering optical phased array chip.

High-speed and robust optical beam-steering device will be the key component for various applications, such as LiDAR (light detection and ranging) and free-space optical communication. Optical phased arrays (OPAs) integrated on semiconductor chips have recently received increasing attention due to the high-speed operation, compactness, and low cost. In particular, indium phosphide (InP)-based OPAs are advantageous for high-output-power applications at 1.55- $\mu \text{m}$ eye-safe wavelength, owing to their capability of monolithically integrating active components, such as high-power lasers and optical amplifiers. In this letter, we design and fabricate, to the best of our knowledge, the largest-scale InP-based OPA and experimentally demonstrate its beam-steering operations. The fabricated OPA consists of 100 waveguides with carrier-injection-based phase shifters, densely integrated on 7 mm $\times5$ mm footprint. A focused beam width of 0.11° is steered across the free spectral range of 8.88°, corresponding to more than 80 resolvable points. This is the largest number of resolvable points obtained by a monolithic InP OPA. The response time of the OPA is confirmed to be less than 16 ns, which is limited by the driver circuit. This work paves the way for realizing compact beam-steering modules for high-speed and high-output-power imaging applications.

Large-Scale Monolithic InP-Based Optical Phased Array

This paper aims to review the state of the art of Light Detection and Ranging (LiDAR) sensors for automotive applications, and particularly for automated vehicles, focusing on recent advances in the field of integrated LiDAR, and one of its key components: the Optical Phased Array (OPA). LiDAR is still a sensor that divides the automotive community, with several automotive companies investing in it, and some companies stating that LiDAR is a ‘useless appendix’. However, currently there is not a single sensor technology able to robustly and completely support automated navigation. Therefore, LiDAR, with its capability to map in 3 dimensions (3D) the vehicle surroundings, is a strong candidate to support Automated Vehicles (AVs). This manuscript highlights current AV sensor challenges, and it analyses the strengths and weaknesses of the perception sensor currently deployed. Then, the manuscript discusses the main LiDAR technologies emerging in automotive, and focuses on integrated LiDAR, challenges associated with light beam steering on a chip, the use of Optical Phased Arrays, finally discussing current factors hindering the affirmation of silicon photonics OPAs and their future research directions.

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A Review and Perspective on Optical Phased Array for Automotive LiDAR

Assisted and automated driving functions are increasingly deployed to support improved safety and efficiency and enhance the driver experience. However, there are still key technical challenges that need to be overcome, such as the degradation of perception sensor data due to noise factors. The quality of data being generated by sensors can directly impact the planning and control of the vehicle, which can affect the vehicle safety. This work builds on a recently proposed framework, analyzing noise factors on automotive light detection and ranging (LiDAR) sensors, and deploys it to camera sensors, focusing on the specific disturbed sensor outputs via a detailed analysis and classification of automotive camera-specific noise sources (30 noise factors are identified and classified in this work). Moreover, the noise factor analysis has identified two omnipresent and independent noise factors (i.e., obstruction and windshield distortion). These noise factors have been modeled to generate noisy camera data; their impact on the perception step, based on deep neural networks (NNs), has been evaluated when the noise factors are applied independently and simultaneously. It is demonstrated that the performance degradation from the combination of noise factors is not simply the accumulated performance degradation from each single factor, which raises the importance of including the simultaneous analysis of multiple noise factors. Thus, the framework can support and enhance the use of simulation for the development and testing of automated vehicles through careful consideration of the noise factors affecting camera data.

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

Given the promising advances in the field of Assisted and Automated Driving, it is expected that the roads of the future will be populated by vehicles driven by computers, partially or fully replacing human drivers. In this scenario, the first stage of the perception-decision-actuation pipeline will likely rely on Deep Neural Networks for understanding the scene around the vehicle. Typical tasks for Deep Neural Networks are object detection and instance segmentation, tasks relying on supervised learning and annotated datasets. As one can imagine, the quality of the labelled dataset strongly affects the performance of the network, and this aspect is investigated in this paper. Annotation quality should be a primary concern in safety-critical tasks, such as Assisted and Automated Driving. This work addresses and classifies some of the mistakes found in a popular automotive dataset. Moreover, some experiments with a Deep Neural Network model were performed to test the effect of these mistakes on network predictions. A set of criteria was established to support the relabelling of the testing dataset which was compared to the original dataset.

Boda Li

Papers

A Review and Perspective on Optical Phased Array for Automotive LiDAR

Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection

Testing ground-truth errors in an automotive dataset for a DNN-based object detector