The three main elements of autonomous vehicles (AV) are orientation, visibility, and decision. This chapter presents an overview of the implementation of visibility-based technologies and methodologies. The chapter first presents two fundamental aspects that are necessary for understanding the main contents. The first aspect is highway geometric design as it relates to sight distance and highway alignment. The second aspect is mathematical basics, including coordinate transformation and visual space segmentation. Details on the Light Detection and Ranging (Lidar) system, which represents the ‘eye’ of the AV are presented. In particular, a new Lidar 3D mapping system, that can be operated on different platforms and modes for a new mapping scheme is described. The visibility methodologies include two types. Infrastructure visibility mainly addresses high-precision maps and sight obstacle detection. Traffic visibility (vehicles, pedestrians, and cyclists) addresses identification of critical positions and visibility estimation. Then, an overview of the decision element (path planning and intelligent car-following) for the movement of AV is presented. The chapter provides important information for researchers and therefore should help to advance road safety for autonomous vehicles.

ABSTRACT:Rice is the main water-consuming crop planted in Egypt Delta. Constrained with the limited water resources, mapping rice is essential for any better water resources management. Xiao(2005) developed an algorithm for rice mapping by studying the dynamics of three vegetation indices the normalized difference vegetation index(NDVI), the Enhanced Vegetation Index(EVI) and the Land surface water index(LSWI). Rice main differentiating feature is being planted in flooded land. Thus moisture sensitive index like LSWI will temporally exceed the EVI or the NDVI signalling rice transplanting. Xiao (2005) utilized MODIS free satellite imagery(500m spatial resolution). However its coarse resolution combined with the Egyptian complex landscape raised the need for the algorithm modification. In this piece of work a low -cost rice mapping algorithm was developed. The multi resolution(MODIS 250m red and near infrared bands) and (MODIS 500m -shortwave infrared and blue bands) were utilized. The arable land was mapped through the utilization of the NDVI and applying it on MODIS 250m (fine spatial resolution) scenes. The MODIS fine temporal resolution (MOD09A1 product) was utilized to study the LSWI, NDVI and EVI dynamics throughout the rice planting season. The non-arable land from MODIS 250m was then used to refine the rice area calculated from the MODIS 500m imagery. The algorithm was applied on the Egypt delta region in years 2008, 2009, and 2010. The mapped rice areas were enhanced from the MODIS 250m arable mapping module and the results of the algorithm were validated against annual areas reports. There was good agreement between the estimated areas from the algorithm and the reports. Inter annual variation in rice areas was successfully mapped. In addition, the rice area and probable transplanting dates conforms to local planting practices. The findings of this study indicate that the algorithm can be used for rice mapping on a timely and frequent manner. * Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

<div>The need for 3D mapping is on the rise to meet the requirements of a growing and diverse group of end-users. Existing 3D mapping systems, which have been classified according to the mode of operation as stationary, mobile and aerial, tend to serve one mode of operation only and are considered cost-prohibitive for many end-users. Unmanned aerial vehicles (UAVs) have experienced rapid growth since their introduction and their usage in 3D mapping is likewise accelerating at a rapid pace. This dissertation presents the design, development and implementation of a LiDAR-based generic 3D mapping system that can be used in the three mapping modes (stationary, mobile and UAV-based). The system provides direct georeferencing capabilities through optimized selected multimodal sensors. A fundamental part of this dissertation is the smart integration of the 3D mapping system components both on the hardware and software levels, along with a new mapping scheme that enables platform-independent deployment ability. </div><div>This research project also presents a rigorous non-linear uncertainty predictive model for the generic developed system and introduces a very low-cost variant of the system to be used in stationary and handheld mode. The developed multipurpose mapping system is tested in different environments for the three modes of operation, demonstrating its practicality, versatility and ease of deployment. To maximize the ease of deployment for diverse end-users, careful consideration is given to the mapping system components so that the developed system is ultra-lightweight, compact, and multipurpose. Additionally, this dissertation proposes a colorization workflow to make use of available optical imagery in the colorization process of the LiDAR point cloud. Lastly, the study compares two different 3D mapping approaches: 3D LiDAR-based mapping and a low-cost optical-based 3D structure from motion (SfM) workflow. The comparison is achieved by performing a real-world case study of digital surface model (DSM) generation by the two aforementioned approaches. Real-world testing that includes qualitative and quantitative validation against accurate state-of-the-art high-end LiDAR equipment proves the successful design, development and deployment of the developed crosscutting LiDAR-based 3D mapping system.</div>

<div>The need for 3D mapping is on the rise to meet the requirements of a growing and diverse group of end-users. Existing 3D mapping systems, which have been classified according to the mode of operation as stationary, mobile and aerial, tend to serve one mode of operation only and are considered cost-prohibitive for many end-users. Unmanned aerial vehicles (UAVs) have experienced rapid growth since their introduction and their usage in 3D mapping is likewise accelerating at a rapid pace. This dissertation presents the design, development and implementation of a LiDAR-based generic 3D mapping system that can be used in the three mapping modes (stationary, mobile and UAV-based). The system provides direct georeferencing capabilities through optimized selected multimodal sensors. A fundamental part of this dissertation is the smart integration of the 3D mapping system components both on the hardware and software levels, along with a new mapping scheme that enables platform-independent deployment ability. </div><div>This research project also presents a rigorous non-linear uncertainty predictive model for the generic developed system and introduces a very low-cost variant of the system to be used in stationary and handheld mode. The developed multipurpose mapping system is tested in different environments for the three modes of operation, demonstrating its practicality, versatility and ease of deployment. To maximize the ease of deployment for diverse end-users, careful consideration is given to the mapping system components so that the developed system is ultra-lightweight, compact, and multipurpose. Additionally, this dissertation proposes a colorization workflow to make use of available optical imagery in the colorization process of the LiDAR point cloud. Lastly, the study compares two different 3D mapping approaches: 3D LiDAR-based mapping and a low-cost optical-based 3D structure from motion (SfM) workflow. The comparison is achieved by performing a real-world case study of digital surface model (DSM) generation by the two aforementioned approaches. Real-world testing that includes qualitative and quantitative validation against accurate state-of-the-art high-end LiDAR equipment proves the successful design, development and deployment of the developed crosscutting LiDAR-based 3D mapping system.</div>