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Lidar And Camera Fusion In Autonomous Vehicles
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Book Synopsis LiDAR and Camera Fusion in Autonomous Vehicles by : Jie Zhang
Download or read book LiDAR and Camera Fusion in Autonomous Vehicles written by Jie Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: LiDAR and camera can be an excellent complement to the advantages in an autonomous vehicle system. Various fusion methods have been developed for sensor fusion. Due to information lost, the autonomous driving system cannot navigate complex driving scenarios. When integrating the camera and LiDAR data, to account for loss of some detail of characters when using late fusion, we could choose a convolution neural network to fuse the features. However, the current sensor fusion method has low efficiency for the actual self-driving task due to the complex scenarios. To improve the efficiency and effectiveness of context fusion in high density traffic, we propose a new fusion method and architecture to combine the multi-model information after extracting the features from the LiDAR and camera. This new method is able to pay extra attention to features we want by allocating the weight during the feature extractor level.
Book Synopsis Multi-sensor Fusion for Autonomous Driving by : Xinyu Zhang
Download or read book Multi-sensor Fusion for Autonomous Driving written by Xinyu Zhang and published by Springer Nature. This book was released on with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Sensor Fusion for 3D Object Detection for Autonomous Vehicles by : Yahya Massoud
Download or read book Sensor Fusion for 3D Object Detection for Autonomous Vehicles written by Yahya Massoud and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Thanks to the major advancements in hardware and computational power, sensor technology, and artificial intelligence, the race for fully autonomous driving systems is heating up. With a countless number of challenging conditions and driving scenarios, researchers are tackling the most challenging problems in driverless cars. One of the most critical components is the perception module, which enables an autonomous vehicle to "see" and "understand" its surrounding environment. Given that modern vehicles can have large number of sensors and available data streams, this thesis presents a deep learning-based framework that leverages multimodal data - i.e. sensor fusion, to perform the task of 3D object detection and localization. We provide an extensive review of the advancements of deep learning-based methods in computer vision, specifically in 2D and 3D object detection tasks. We also study the progress of the literature in both single-sensor and multi-sensor data fusion techniques. Furthermore, we present an in-depth explanation of our proposed approach that performs sensor fusion using input streams from LiDAR and Camera sensors, aiming to simultaneously perform 2D, 3D, and Bird's Eye View detection. Our experiments highlight the importance of learnable data fusion mechanisms and multi-task learning, the impact of different CNN design decisions, speed-accuracy tradeoffs, and ways to deal with overfitting in multi-sensor data fusion frameworks.
Book Synopsis The Future is Autonomous by : Phillip Wilcox
Download or read book The Future is Autonomous written by Phillip Wilcox and published by . This book was released on 2021-03 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Who will win the race to develop the autonomous vehicle? Making predictions about technology, particularly technology as revolutionary as the autonomous vehicle, can be challenging. The Future is Autonomous: The U.S. and China Race to Develop the Driverless Car explores a number of key factors that will decide who will emerge victorious. In this book you will learn about: The major technological difficulties that must be overcome for a self-driving car to drive safely. The innovative companies that are creating new business models to commercialize autonomous vehicles. The political hurdles that both the U.S. and China must face to establish a common set of standards for autonomous vehicles both domestically and globally. And so much more! This book is a must read for anyone interested in the future of the automotive industry, cutting-edge technology, and keen political analysis. There is little doubt that whoever wins the race to develop the autonomous vehicle will have substantial influence in the industry for decades. No matter which superpower comes out on top, the biggest winner of all will be the consumer.
Book Synopsis Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles by : Cristina P. Magnusson
Download or read book Unsettled Topics Concerning Coating Detection by LiDAR in Autonomous Vehicles written by Cristina P. Magnusson and published by . This book was released on 2021-01-18 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles (AVs) utilize multiple devices, like high-resolution cameras and radar sensors, to interpret the driving environment and achieve full autonomy. One of these instruments-the light detection and ranging (LiDAR) sensor-functions like radar, but utilizes pulsed infrared (IR) light, typically at wavelengths of 905 nm or 1,550 nm. The LiDAR sensor receives the reflected light from objects and calculates each object's distance and position. In current vehicles, the exterior automotive paint system covers an area larger than any other exterior material. Therefore, understanding how LiDAR wavelengths interact with other vehicles' coatings is extremely important for the safety of future automated driving technologies. Some coatings are more easily detected by LiDAR than others. In general, dark colors can absorb as much as 95% of the incident LiDAR intensity, reducing the amount of signal reflected toward the sensor. White cars are more easily detected as they exhibit high IR reflectivity. Many other factors like gloss level, effect pigments, and refinishes can affect reflectivity and even blind LiDAR sensors. On the other hand, several variables define overall LiDAR and perception system performance, including IR reflectivity of paint but also the target object's geometry, the type of LiDAR technology employed, angle of the target surface, environmental conditions, and sensor fusion software architecture. Sensing Technologies and Materials are two different industries that have not directly interacted in the perception and system sense. With the new applications in the AV industry, approaches need to be taken in a multidisciplinary way to ensure a reliable and safe technology for the future. This report provides a transversal view of the different industry segments from pigment and coating manufacturers to LiDAR component and vehicle system development and integration, and a structured decomposition of the different variables and technologies involved. NOTE: SAE EDGE Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. These reports are not intended to resolve the challenges they identify or close any topic to further scrutiny.
Book Synopsis Creating Autonomous Vehicle Systems by : Shaoshan Liu
Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.
Book Synopsis 2018 26th Telecommunications Forum (TELFOR) by : IEEE Staff
Download or read book 2018 26th Telecommunications Forum (TELFOR) written by IEEE Staff and published by . This book was released on 2018-11-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: TELFOR has the purpose to consider important scientific, engineering, professional, social, economical and legal aspects of Telecommunications and Information Technology The work of TELFOR is organized through plenary sessions, invited papers, lectures, regular authors and students paper sessions, tutorials, seminars, workshops, technical, information and commercial presentations, exhibitions, etc
Book Synopsis Light-weighted Deep Learning for Lidar and Visual Odometry Fusion in Autonomous Driving by : Dingnan Zhang
Download or read book Light-weighted Deep Learning for Lidar and Visual Odometry Fusion in Autonomous Driving written by Dingnan Zhang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Visual odometry is a prevalent way to deal with the relative localization problem, which is experiencing rapid development applied to autonomous vehicles. Achieving rapid pose estimation with high accuracy is a challenging task due to the safety requirements in the complex dynamic driving environment. Visual odometry algorithms are mostly designed based on a pipeline structure based on feature detection, feature matching, motion estimation, bundle adjustment, etc. These existing algorithms usually need to be individually designed and fine-tuned to have an acceptable result. A new measurement system, monocular visual odometry based on a neural network, is used to extract features and perform feature matching. It estimates poses directly from a sequence form videos without any adopting module. Neural network learns effective feature representation automatically. Pose estimation is usually applied in automated driving systems. The efficiency of measurement is the primary task due to the safety requirement in a complex environment on the street. An efficient model is used in this system instead of a traditional convolutional neural network. Some applications on embedded platforms, such as robotics and autonomous driving have limited hardware resources. Autonomous vehicles have equipped with different sensors to perceive the environment. They need a light-weighted, low-latency network model with 3 camera-LiDAR fusion. This proposed model can efficiently reduce the computational cost of the model, while the accuracy is improved.
Book Synopsis Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles by : Jesse Sol Levinson
Download or read book Automatic Laser Calibration, Mapping, and Localization for Autonomous Vehicles written by Jesse Sol Levinson and published by Stanford University. This book was released on 2011 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents several related algorithms that enable important capabilities for self-driving vehicles. Using a rotating multi-beam laser rangefinder to sense the world, our vehicle scans millions of 3D points every second. Calibrating these sensors plays a crucial role in accurate perception, but manual calibration is unreasonably tedious, and generally inaccurate. As an alternative, we present an unsupervised algorithm for automatically calibrating both the intrinsics and extrinsics of the laser unit from only seconds of driving in an arbitrary and unknown environment. We show that the results are not only vastly easier to obtain than traditional calibration techniques, they are also more accurate. A second key challenge in autonomous navigation is reliable localization in the face of uncertainty. Using our calibrated sensors, we obtain high resolution infrared reflectivity readings of the world. From these, we build large-scale self-consistent probabilistic laser maps of urban scenes, and show that we can reliably localize a vehicle against these maps to within centimeters, even in dynamic environments, by fusing noisy GPS and IMU readings with the laser in realtime. We also present a localization algorithm that was used in the DARPA Urban Challenge, which operated without a prerecorded laser map, and allowed our vehicle to complete the entire six-hour course without a single localization failure. Finally, we present a collection of algorithms for the mapping and detection of traffic lights in realtime. These methods use a combination of computer-vision techniques and probabilistic approaches to incorporating uncertainty in order to allow our vehicle to reliably ascertain the state of traffic-light-controlled intersections.
Book Synopsis Advances in Computational Intelligence Techniques by : Shruti Jain
Download or read book Advances in Computational Intelligence Techniques written by Shruti Jain and published by Springer Nature. This book was released on 2020-02-20 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights recent advances in computational intelligence for signal processing, computing, imaging, artificial intelligence, and their applications. It offers support for researchers involved in designing decision support systems to promote the societal acceptance of ambient intelligence, and presents the latest research on diverse topics in intelligence technologies with the goal of advancing knowledge and applications in this rapidly evolving field. As such, it offers a valuable resource for researchers, developers and educators whose work involves recent advances and emerging technologies in computational intelligence.
Book Synopsis A Novel Fusion Technique for 2D LIDAR and Stereo Camera Data Using Fuzzy Logic for Improved Depth Perception by : Harsh Saksena
Download or read book A Novel Fusion Technique for 2D LIDAR and Stereo Camera Data Using Fuzzy Logic for Improved Depth Perception written by Harsh Saksena and published by . This book was released on 2021 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: Obstacle detection, avoidance and path finding for autonomous vehicles requires precise information of the vehicle's system environment for faultless navigation and decision making. As such vision and depth perception sensors have become an integral part of autonomous vehicles in the current research and development of the autonomous industry. The advancements made in vision sensors such as radars, Light Detection And Ranging (LIDAR) sensors and compact high resolution cameras is encouraging, however individual sensors can be prone to error and misinformation due to environmental factors such as scene illumination, object reflectivity and object transparency. The application of sensor fusion in a system, by the utilization of multiple sensors perceiving similar or relatable information over a network, is implemented to provide a more robust and complete system information and minimize the overall perceived error of the system. 3D LIDAR and monocular camera are the most commonly utilized vision sensors for the implementation of sensor fusion. 3D LIDARs boast a high accuracy and resolution for depth capturing for any given environment and have a broad range of applications such as terrain mapping and 3D reconstruction. Despite 3D LIDAR being the superior sensor for depth, the high cost and sensitivity to its environment make it a poor choice for mid-range application such as autonomous rovers, RC cars and robots. 2D LIDARs are more affordable, easily available and have a wider range of applications than 3D LIDARs, making them the more obvious choice for budget projects. The primary objective of this thesis is to implement a smart and robust sensor fusion system using 2D LIDAR and a stereo depth camera to capture depth and color information of an environment. The depth points generated by the LIDAR are fused with the depth map generated by the stereo camera by a Fuzzy system that implements smart fusion and corrects any gaps in the depth information of the stereo camera. The use of Fuzzy system for sensor fusion of 2D LIDAR and stereo camera is a novel approach to the sensor fusion problem and the output of the fuzzy fusion provides higher depth confidence than the individual sensors provide. In this thesis, we will explore the multiple layers of sensor and data fusion that have been applied to the vision system, both on the camera and lidar data individually and in relation to each other. We will go into detail regarding the development and implementation of fuzzy logic based fusion approach, the fuzzification of input data and the method of selection of the fuzzy system for depth specific fusion for the given vision system and how fuzzy logic can be utilized to provide information which is vastly more reliable than the information provided by the camera and LIDAR separately.
Book Synopsis Intelligent Transport Systems by : Asier Perallos
Download or read book Intelligent Transport Systems written by Asier Perallos and published by John Wiley & Sons. This book was released on 2015-12-14 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: INTELLIGENT TRANSPORT SYSTEMS TECHNOLOGIES AND APPLICATIONS This book provides a systematic overview of Intelligent Transportation Systems (ITS), offering an insight into the reference architectures developed within the main research projects. It delves into each of the layers of such architectures, from physical to application layer, describing the technological issues which are being currently faced by some of the most important ITS research groups. The book concludes with some end-user services and applications deployed by industrial partners. The book is a well-balanced combination of academic contributions and industrial applications in the field of Intelligent Transportation Systems. It includes the most representative technologies and research results achieved by some of the most relevant research groups working on ITS, collated to show the chances of generating industrial solutions to be deployed in real transportation environments.
Book Synopsis 3D Object Detection and Tracking for Autonomous Vehicles by : Su Pang
Download or read book 3D Object Detection and Tracking for Autonomous Vehicles written by Su Pang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving systems require accurate 3D object detection and tracking to achieve reliable path planning and navigation. For object detection, there have been significant advances in neural networks for single-modality approaches. However, it has been surprisingly difficult to train networks to use multiple modalities in a way that demonstrates gain over single-modality networks. In this dissertation, we first propose three networks for Camera-LiDAR and Camera-Radar fusion. For Camera-LiDAR fusion, CLOCs (Camera-LiDAR Object Candidates fusion) and Fast-CLOCs are presented. CLOCs fusion provides a multi-modal fusion framework that significantly improves the performance of single-modality detectors. CLOCs operates on the combined output candidates before Non-Maximum Suppression (NMS) of any 2D and any 3D detector, and is trained to leverage their geometric and semantic consistencies to produce more accurate 3D detection results. Fast-CLOCs can run in near real-time with less computational requirements compared to CLOCs. Fast-CLOCs eliminates the separate heavy 2D detector, and instead uses a 3D detector-cued 2D image detector (3D-Q-2D) to reduce memory and computation. For Camera-Radar fusion, we propose TransCAR, a Transformer-based Camera-And-Radar fusion solution for 3D object detection. The cross-attention layer within the transformer decoder can adaptively learn the soft-association between the radar features and vision queries instead of hard-association based on sensor calibration only. Then, we propose to solve the 3D multiple object tracking (MOT) problem for autonomous driving applications using a random finite set-based (RFS) Multiple Measurement Models filter (RFS-M3). In particular, we propose multiple measurement models for a Poisson multi-Bernoulli mixture (PMBM) filter in support of different application scenarios. Our RFS-M3 filter can naturally model these uncertainties accurately and elegantly. We combine learning-based detections with our RFS-M3 tracker by incorporating the detection confidence score into the PMBM prediction and update step. We have evaluated our CLOCs, Fast-CLOCs and TransCAR fusion-based 3D detector and RFS-M3 3D tracker using challenging datasets including KITTI, nuScenes, Argoverse and Waymo that are released by academia and industry leaders. Superior experimental results demonstrated the effectiveness of the proposed approaches.
Book Synopsis Automated Driving by : Daniel Watzenig
Download or read book Automated Driving written by Daniel Watzenig and published by Springer. This book was released on 2016-09-23 with total page 619 pages. Available in PDF, EPUB and Kindle. Book excerpt: The main topics of this book include advanced control, cognitive data processing, high performance computing, functional safety, and comprehensive validation. These topics are seen as technological bricks to drive forward automated driving. The current state of the art of automated vehicle research, development and innovation is given. The book also addresses industry-driven roadmaps for major new technology advances as well as collaborative European initiatives supporting the evolvement of automated driving. Various examples highlight the state of development of automated driving as well as the way forward. The book will be of interest to academics and researchers within engineering, graduate students, automotive engineers at OEMs and suppliers, ICT and software engineers, managers, and other decision-makers.
Book Synopsis Recent Development in Optoelectronic Devices by : Ruby Srivastava
Download or read book Recent Development in Optoelectronic Devices written by Ruby Srivastava and published by BoD – Books on Demand. This book was released on 2018-08-29 with total page 143 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book "Recent Developments in Optoelectronic Devices" is about the latest developments in optoelectronics. This book is divided into three categories: light emitting devices, sensors, and light harvesters. This book also discusses the theoretical aspects of device design for iridium complexes as organic light emitting diodes (OLEDs), strategies for developing novel nanostructured materials, silicon-rich oxide (SRO) electroluminescent devices, and multifunctional optoelectronic devices developed on resistive switching effects. The worldwide participation of authors has contributed to the unifying effect of science. Furthermore, interested readers will also find information on the screen printed technology using semiconductor devices, nonlinear phenomena in quantum devices, experimental set up of optoelectronics flexible logic gate to realize logic operations, autonomous vehicles, and the latest developments in perovskites as solar cells.
Book Synopsis Sensor Fusion in Localization, Mapping and Tracking by : Constantin Wellhausen
Download or read book Sensor Fusion in Localization, Mapping and Tracking written by Constantin Wellhausen and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making autonomous driving possible requires extensive information about the surroundings as well as the state of the vehicle. While specific information can be obtained through singular sensors, a full estimation requires a multi sensory approach, including redundant sources of information to increase robustness. This thesis gives an overview of tasks that arise in sensor fusion in autonomous driving, and presents solutions at a high level of detail, including derivations and parameters where required to enable re-implementation. The thesis includes theoretical considerations of the approaches as well as practical evaluations. Evaluations are also included for approaches that did not prove to solve their tasks robustly. This follows the belief that both results further the state of the art by giving researchers ideas about suitable and unsuitable approaches, where otherwise the unsuitable approaches may be re-implemented multiple times with similar results. The thesis focuses on model-based methods, also referred to in the following as classical methods, with a special focus on probabilistic and evidential theories. Methods based on deep learning are explicitly not covered to maintain explainability and robustness which would otherwise strongly rely on the available training data. The main focus of the work lies in three main fields of autonomous driving: localization, which estimates the state of the ego-vehicle, mapping or obstacle detection, where drivable areas are identified, and object detection and tracking, which estimates the state of all surrounding traffic participants. All algorithms are designed with the requirements of autonomous driving in mind, with a focus on robustness, real-time capability and usability of the approaches in all potential scenarios that may arise in urban driving. In localization the state of the vehicle is determined. While traditionally global positioning systems such as a Global Navigation Satellite System (GNSS) are often used for this task, they are prone to errors and may produce jumps in the position estimate which may cause unexpected and dangerous behavior. The focus of research in this thesis is the development of a localization system which produces a smooth state estimate without any jumps. For this two localization approaches are developed and executed in parallel. One localization is performed without global information to avoid jumps. This however only provides odometry, which drifts over time and does not give global positioning. To provide this information the second localization includes GNSS information, thus providing a global estimate which is free of global drift. Additionally the use of LiDAR odometry for improving the localization accuracy is evaluated. For mapping the focus of this thesis is on providing a computationally efficient mapping system which is capable of being used in arbitrarily large areas with no predefined size. This is achieved by mapping only the direct environment of the vehicle, with older information in the map being discarded. This is motivated by the observation that the environment in autonomous driving is highly dynamic and must be mapped anew every time the vehicles sensors observe an area. The provided map gives subsequent algorithms information about areas where the vehicle can or cannot drive. For this an occupancy grid map is used, which discretizes the map into cells of a fixed size, with each cell estimating whether its corresponding space in the world is occupied. However the grid map is not created for the entire area which could potentially be visited, as this may be very large and potentially impossible to represent in the working memory. Instead the map is created only for a window around the vehicle, with the vehicle roughly in the center. A hierarchical map organization is used to allow efficient moving of the window as the vehicle moves through an area. For the hierarchical map different data structures are evaluated for their time and space complexity in order to find the most suitable implementation for the presented mapping approach. Finally for tracking a late-fusion approach to the multi-sensor fusion task of estimating states of all other traffic participants is presented. Object detections are obtained from LiDAR, camera and Radar sensors, with an additional source of information being obtained from vehicle-to-everything communication which is also fused in the late fusion. The late fusion is developed for easy extendability and with arbitrary object detection algorithms in mind. For the first evaluation it relies on black box object detections provided by the sensors. In the second part of the research in object tracking multiple algorithms for object detection on LiDAR data are evaluated for the use in the object tracking framework to ease the reliance on black box implementations. A focus is set on detecting objects from motion, where three different approaches are evaluated for motion estimation in LiDAR data: LiDAR optical flow, evidential dynamic mapping and normal distribution transforms. The thesis contains both theoretical contributions and practical implementation considerations for the presented approaches with a high degree of detail including all necessary derivations. All results are implemented and evaluated on an autonomous vehicle and real-world data. With the developed algorithms autonomous driving is realized for urban areas.
Book Synopsis Autonomous Vehicle Lidar by : Kai Zhou
Download or read book Autonomous Vehicle Lidar written by Kai Zhou and published by . This book was released on 2019-12-31 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: The largest high-tech companies and leading automobile manufacturers in the world have unleashed torrents of effort and capital to position themselves for the arrival of autonomous vehicles. What is the fuss about? What is at stake? What are the leading sensor technologies? What is meant by "flash lidar" or "time-of-flight" sensors? With no less than 40 - 50 lidar companies vying to create mainstream automotive sensors, the climate is unique for young scientists and engineers to enter the field. What are the alliances forming between the companies, and how are they shifting? Who are current incumbents in the field? This tutorial text aims to introduce a technical but nonspecialist reader to autonomous vehicle lidar, starting from the fundamental physics of lidar and motivation for its application to autonomous vehicle systems. We will then introduce time of flight design concepts, following the light pathway through the source and transmitter optics to the photodetector. Next two distinct timing methods will be introduced, followed up by a brief discussion of beam steering. After finishing this text, the reader should be prepared to enter into laboratory explorations on the topic.
