Automotive LiDAR Research Articles

High-peak-power nanosecond pulse laser at 915 nm based on all-fiber structure

We demonstrate an all-fiber high-peak-power nanosecond pulse laser operating at 915 nm. The nanosecond pulse laser source is consisting of a 915 nm modulated laser diode and 4-stage Nd-doped fiber amplifiers. A maximum output power of 2.01 W is achieved with a peak power of 2.2 kW. The peak-to-peak fluctuation is less than ±0.2 % and the RMS is 0.084 %. Additionally, the beam quality at output power of 2.01 W is measured to be Mx2 = 1.09, My2 = 1.08, indicative of excellent beam characteristics in the output signal. To the best of our knowledge, it is the first report about high-peak-power Nd-doped fiber laser at 915 nm based on all-fiber structure. This work provides a good candidate for the Automotive Lidar with high imaging resolution and detection accuracy.

Geometric Fidelity Requirements for Meshes in Automotive Lidar Simulation

The perception of vegetation is a critical aspect of off-road autonomous navigation, and consequentially a critical aspect of the simulation of autonomous ground vehicles (AGVs). Representing vegetation with triangular meshes requires detailed geometric modeling that captures the intricacies of small branches and leaves. In this work, we propose to answer the question, “What degree of geometric fidelity is required to realistically simulate lidar in AGV simulations?” To answer this question, in this work we present an analysis that determines the required geometric fidelity of digital scenes and assets used in the simulation of AGVs. Focusing on vegetation, we use a comparison of the real and simulated perceived distribution of leaf orientation angles in lidar point clouds to determine the number of triangles required to reliably reproduce realistic results. By comparing real lidar scans of vegetation to simulated lidar scans of vegetation with a variety of geometric fidelities, we find that digital tree models (meshes) need to have a minimum triangle density of >1600 triangles per cubic meter in order to accurately reproduce the geometric properties of lidar scans of real vegetation, with a recommended triangle density of 11,000 triangles per cubic meter for best performance. Furthermore, by comparing these experiments to past work investigating the same question for cameras, we develop a general “rule-of-thumb” for vegetation mesh fidelity in AGV sensor simulation.

Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors

With current advances in automated driving, optical sensors like cameras and LiDARs are playing an increasingly important role in modern driver assistance systems. However, these sensors face challenges from adverse weather effects like fog and precipitation, which significantly degrade the sensor performance due to scattering effects in its optical path. Consequently, major efforts are being made to understand, model, and mitigate these effects. In this work, the reverse research question is investigated, demonstrating that these measurement effects can be exploited to predict occurring weather conditions by using state-of-the-art deep learning mechanisms. In order to do so, a variety of models have been developed and trained on a recorded multiseason dataset and benchmarked with respect to performance, model size, and required computational resources, showing that especially modern vision transformers achieve remarkable results in distinguishing up to 15 precipitation classes with an accuracy of 84.41% and predicting the corresponding precipitation rate with a mean absolute error of less than 0.47 mm/h, solely based on measurement noise. Therefore, this research may contribute to a cost-effective solution for characterizing precipitation with a commercial Flash LiDAR sensor, which can be implemented as a lightweight vehicle software feature to issue advanced driver warnings, adapt driving dynamics, or serve as a data quality measure for adaptive data preprocessing and fusion.

Fault Prediction and Estimation of Automotive LiDAR Signals Using Transfer Learning-Based Domain Generalization

Fault Prediction and Estimation of Automotive LiDAR Signals Using Transfer Learning-Based Domain Generalization

PAFNet: Pillar Attention Fusion Network for Vehicle–Infrastructure Cooperative Target Detection Using LiDAR

With the development of autonomous driving, consensus is gradually forming around vehicle–infrastructure cooperative (VIC) autonomous driving. The VIC environment-sensing system uses roadside sensors in collaboration with automotive sensors to capture traffic target information symmetrically from both the roadside and the vehicle, thus extending the perception capabilities of autonomous driving vehicles. However, the current target detection accuracy for feature fusion based on roadside LiDAR and automotive LiDAR is relatively low, making it difficult to satisfy the sensing requirements of autonomous vehicles. This paper proposes PAFNet, a VIC pillar attention fusion network for target detection, aimed at improving LiDAR target detection accuracy under feature fusion. The proposed spatial and temporal cooperative fusion preprocessing method ensures the accuracy of the fused features through frame matching and coordinate transformation of the point cloud. In addition, this paper introduces the first anchor-free method for 3D target detection for VIC feature fusion, using a centroid-based approach for target detection. In the feature fusion stage, we propose the grid attention feature fusion method. This method uses the spatial feature attention mechanism to fuse the roadside and vehicle-side features. The experiment on the DAIR-V2X-C dataset shows that PAFNet achieved a 6.92% higher detection accuracy in 3D target detection than FFNet in urban scenes.

Single Photon Detectors for Automotive LiDAR Applications: State-of-the-Art and Research Challenges

Single Photon Detectors for Automotive LiDAR Applications: State-of-the-Art and Research Challenges

A range ambiguity classification algorithm for automotive LiDAR based on FPGA platform acceleration

In the past decade, the automotive light detection and ranging (LiDAR) has been experiencing a rapid expansion stage. Many researchers have been involved in the research of LiDARs and have installed it in vehicles as a means of enhancing autopilot capabilities. Compared with a traditional millimeter wave radar, LiDARs have many advantages such as the high imaging resolution, long measurement range, and the ability to reconstruct 3D information around the vehicle. These features make LiDARs one of the crucial research hotspots in the field of autopilot. The basic principles of LiDARs are the same as those of a laser rangefinder. The distance information can be obtained by locating the echo instant corresponding to the laser emission moment. But if the interval between two adjacent laser pulses is extremely narrow, the regions of the light emission and echo will be overlapped. Therefore, a range ambiguity will occur and the distance information calculation process will become abnormal. Besides, the high resolution of LiDARs is also characterized by its extremely high emissions frequency. Whilst the information about the surrounding environment of an automotive car can be retrieved more accurately, it means that the possibility of range ambiguity is also increasing at the same time. In this paper, we propose an algorithm for solving the range ambiguity problem of the LiDARs based on the concept of classification and can be accelerated by the FPGA approach, for the first time in the field of an automotive LiDAR. The algorithm can be performed by employing a single wavelength pulsed laser and can be specifically optimized for the demands of field programmable gate arrays (FPGAs). While guaranteeing the high resolution of LiDARs, the attenuation of the measurement ability should exceed due to the occurrence of range ambiguity. It can also match the demand for the processing speed of large amounts of point cloud information data. Through controlling the cost of the whole device, the performance of the LiDAR can be greatly improved. The result of this paper might provide a bright future of automotive LiDARs with the high data processing efficiency and the high resolution at the same time.

Design and simulation of a near-infrared enhanced Si-based SPAD for an automotive LiDAR.

A near-infrared (NIR)-enhanced single-photon avalanche diode (SPAD) with a retrograded NM/XP junction for an automotive LiDAR was designed based on CSMC 0.18µm BCD technology. A 3µm depth NM/XP junction embedded in a lightly doped deep p-well (DP) improves the absorption efficiency in the NIR regime; the photo-generated electrons generated in the depletion region are efficiently collected into the central multiplication region by a drift process, and then the impact ionization is triggered by the strong field, resulting in a high photon detection efficiency (PDE). Additionally, the deep NM/XP junction and the buried layer effectively isolate the dark noise originating from the interface and the substrate. The SPAD was initially simulated by numerical calculation, and then was evaluated with active quench/reset electronics in a circuit simulator. The results revealed that the SPAD with an active area of 314µm 2 achieves a PDE of 16.2% at 905nm and a dark count rate (DCR) of 1.46H z/µm 2, with an excess bias of 5V at room temperature. The designed SPAD is well suited for the low-cost, miniaturized automotive LiDAR.

Adaptive point cloud acquisition and upsampling for automotive lidar.

One of the crucial factors in achieving a higher level of autonomy of self-driving vehicles is a sensor capable of acquiring accurate and robust information about the environment and other participants in traffic. In the past few decades, various types of sensors have been used for this purpose, such as cameras registering visible, near-infrared, and thermal parts of the spectrum, as well as radars, ultrasonic sensors, and lidar. Due to their high range, accuracy, and robustness, lidars are gaining popularity in numerous applications. However, in many cases, their spatial resolution does not meet the requirements of the application. To solve this problem, we propose a strategy for better utilization of the available points. In particular, we propose an adaptive paradigm that scans the objects of interest with increased resolution, while the background is scanned using a lower point density. Initial region proposals are generated using an object detector that relies on an auxiliary camera. Such a strategy improves the quality of the representation of the object, while retaining the total number of projected points. The proposed method shows improvements compared to regular sampling in terms of the quality of upsampled point clouds.

Technical concepts of automotive LiDAR sensors: a review

Automotive LiDAR sensors are seen by many as the enabling technology for higher-level autonomous driving functionalities. Different concepts to design such a sensor can be found in the industry. Some have already been integrated into consumer cars while many others promise to be in mass production soon to become cost-effective enough for broad deployment. However, automotive LiDAR sensors are still evolving and a variety of sensor designs are pursued by different companies. Here, we construct the automotive LiDAR design space to visually depict system design options for these sensors. Subsequently, we exemplify the concepts with drawings that can be found in published patent applications (focusing on scanning mechanisms and scan patterns) before discussing their advantages and challenges.

In-Field Measurement and Methodology for Modeling and Validation of Precipitation Effects on Solid-State LiDAR Sensors

There is a strong demand for high fidelity sensor models which are capable of simulating realistic automotive sensor perception of Radar, LiDAR and camera sensors in real time, in order to validate advanced driving assistance functions like lane change assist (LCA), automated emergency breaking (AEB), or even path planning virtually. For central data fusion the sensor models need to deliver realistic, artificial sensor raw data. In especially, optical sensors are heavily influenced by precipitation, fog and sun irradiance. However, most LiDAR models lack the capability of replicating the impact of specific weather characteristics. Furthermore, there is – in contrast to numerous publicly available LiDAR datasets – a strong lack of datasets which are annotated with quantitative weather data such as the precipitation rate or meteorological visibility in order to develop and validate such models. Hence, within this work, an automated infrastructure is setup to measure time-correlated LiDAR and weather data to develop and calibrate weather models. The effects of varying precipitation rates on an automotive Flash LiDAR system is demonstrated based on in-field measurements and a respective modeling methodology is developed. Based on the in-field measurement data, raw data LiDAR models can be developed which augment virtual LiDAR data obtained from raytracing capable driving simulation suits as well as real data, recorded under ideal weather conditions.

Concept of an Automotive LiDAR Target Simulator for Direct Time-of-Flight LiDAR

Autonomous driving can make traffic safer by reducing human errors. Different sensor types in autonomous vehicles could introduce additional technical failures. We offer a target simulator testing LiDAR systems under automotive conditions. Therefore, data are projected over-the-air by laser signals on the LiDAR detector. This work presents a concept of a LiDAR target simulator with regards to LiDAR systems using the direct time-of-flight principle. We develope design considerations for a screen discussing undesired screen reflections, a curved screen form and the positioning of light sources on this screen. As one main solution, we introduce a concept of an antireflective screen. For the scenario simulation, we derive a model delivering the required optical power representing a simulated target, which is combined with the simulated time-of-flight. Considering no prior knowledge of the LiDAR system under test, we discuss the required calibration data and timing resolution. Thereby, we suggest an optimized time-of-flight concept requiring only one optical trigger to determine the LiDAR measurement start. All requirements are supported by calculated example parameters based on real LiDAR systems. Altogether, we discuss main challenges and possible solutions of our LiDAR target simulator, which will allow a safer and more efficient development of LiDAR systems.

SPAD depth sensor for automotive LiDAR systems

SPAD depth sensor for automotive LiDAR systems

Performance Evaluation According to Material Characteristics of Road Safety Facilities Using Automotive LiDAR Sensors

Performance Evaluation According to Material Characteristics of Road Safety Facilities Using Automotive LiDAR Sensors

Moving Objects Tracking Based on Geometric Model-Free Approach With Particle Filter Using Automotive LiDAR

In this paper, we propose a Geometric Model-Free Approach with a Particle Filter (GMFA-PF) through the use of automotive LiDAR for real-time tracking of moving objects within an urban driving environment. GMFA-PF proved to be lightweight, capable of finishing the process within the sensing period of the LiDAR on a single CPU. The proposed GMFA-PF tracks and estimates moving objects without any assumptions on the geometry of the target. This approach enables efficient tracking of multiple object classes, with robustness to a sparse point cloud. Point cloud on moving objects is classified via the predicted Static Obstacle Map (STOM). A likelihood field is generated through the classified point cloud and is used in particle filtering to estimate the moving object’s pose, shape, and speed. Quantitative and qualitative comparisons - with Geometric Model-Based Tracking (MBT), Deep Neural Network (DNN), and GMFA - are performed for GMFA-PF using urban driving and scenario driving data gathered on an autonomous vehicle fitted with close-to-market sensors. The proposed approach shows robust tracking and accurate estimation performance in both sparse and dense point clouds; GMFA-PF achieves improved tracking performance in dense traffic and reduces yaw estimation delay compared to the others. Autonomous vehicles with GMFA-PF demonstrated auto-nomous driving on urban roads.

Measuring the Influence of Environmental Conditions on Automotive Lidar Sensors.

Safety validation of automated driving functions is a major challenge that is partly tackled by means of simulation-based testing. The virtual validation approach always entails the modeling of automotive perception sensors and their environment. In the real world, these sensors are exposed to adverse influences by environmental conditions such as rain, fog, snow, etc. Therefore, such influences need to be reflected in the simulation models. In this publication, a novel data set is introduced and analyzed. This data set contains lidar data with synchronized reference measurements of weather conditions from a stationary long-term experiment. Recorded weather conditions comprise fog, rain, snow, and direct sunlight. The data are analyzed by pairing lidar values, such as the number of detections in the atmosphere, with weather parameters such as rain rate in mm/h. This results in expectation values, which can directly be utilized for stochastic modeling or model calibration and validation. The results show vast differences in the number of atmospheric detections, range distribution, and attenuation between the different sensors of the data set.

Integrated Coherent Tunable Laser (ICTL) With Ultra-Wideband Wavelength Tuning and Sub-100 Hz Lorentzian Linewidth

This paper describes the design, fabrication, and record performance of a new class of ultra-wideband wavelength tuning, ultra-low noise semiconductor laser, the Integrated Coherent Tunable Laser (ICTL). The ICTL device is designed for, and fabricated in, a CMOS foundry based Silicon Photonics platform, utilizing heterogeneous integration of III-V material to create the integrated gain section of the laser–enabling high-volume mass-market manufacturing at low cost and with high reliability. The ICTL incorporates three or more ultra-low loss micro-ring resonators, with large ring size, in a Sagnac loop reflector geometry, creating exceptional laser reflector performance, plus an extended laser cavity length that enables highly-coherent output; ultra-low linewidth and phase noise. This paper describes record integrated laser performance; 118 nm wavelength tuning, covering S-, C- and L-bands, with Lorentzian linewidth <100 Hz, and with excellent relative intensity noise (RIN) of ≤ −155 dBc/Hz. The remarkable performance of the ICTL device, coupled with the high volume/low cost capability of the Silicon Photonics platform enables next-generation applications including ultra-wideband WDM transmission systems, fiber-optic and medical-wearable sensing systems, and automotive FMCW LiDAR systems utilizing wavelength scanning.

Automatic Inspection System for Automotive LiDAR Alignment Using a Cubic Target

This study aimed to develop an automatic and accurate inspection system for LiDAR alignment for application in the automobile industry. The proposed inspection system is a one-shot and static method that uses only a single cubic-shaped target at a close range of less than 2 m. The LiDAR pose from the target frame, and consequently from the vehicle body, is estimated by using the 3D positions of the vertices of the cubic target. Instead of directly extracting the vertices from the point cloud data, the points are estimated accurately through a series of processes that find the best-fit orthogonal planes and their intersections. The proposed method can be efficiently applied to low-resolution LiDAR and provides higher accuracy and precision than a similar static inspection method that uses a planar target (Oh <i>et al.</i>, 2021). Experimental evaluation of the proposed method with a low-resolution LiDAR (Velodyne VLP-16) resulted in measurement errors of less than 0.04&#x00B0; and 1 mm for rotation and translation alignment, respectively. Therefore, the designed inspection system is practical for in-factory applications because of its one-shot static methodology that uses only a simple cubic target and detection of the alignment error with high accuracy.

Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit, and Effects on the Point Cloud

Lidar is an important component of the perception suite for automated systems. The effects of vibration on lidar point clouds are mostly unknown, despite the lidar&#x2019;s wide adaption and usual application under conditions where vibration occurs frequently. In this study, we performed controlled vibration tests from 6 to 2000 Hz at 9 and 12 m/s2 in vertical direction on the automotive lidar OS1-64 by Ouster. An information loss emerged which is mostly independent from frequency and acceleration. The loss of points is randomly distributed and does not correlate with range, intensity, or ring number (the horizontal line of the rotating lidar unit). The resonance frequency of 1426 Hz proved to be unproblematic as no pronounced negative effects on the point cloud could be identified. For vibration detection, the internal Inertial Measurement Unit (IMU) of the OS1-64 is accurate and sufficient for vibrations up to 50 Hz. Above 50 Hz, external IMUs would be required for vibration detection. Counting the number of points on a target close to the edges was investigated as an exemplary way to detect vibration purely based on the point cloud, i.e., independent of the lidar&#x2019;s IMU.

SPAD ranging sensor for automotive LiDAR system

SPAD ranging sensor for automotive LiDAR system