Ensuring the safe and efficient operation of CAVs relies heavily on the software framework used. A software framework needs to ensure real-time properties, reliable communication, and efficient resource utilization. Furthermore, a software framework needs to enable seamless transition between testing stages, from simulation to small-scale to full-scale experiments. In this paper, we survey prominent software frameworks used for in-vehicle and inter-vehicle communication in CAVs. We analyze these frameworks regarding opportunities and challenges, such as their real-time properties and transitioning capabilities. Additionally, we delve into the tooling requirements necessary for addressing the associated challenges. We illustrate the practical implications of these challenges through case studies focusing on critical areas such as perception, motion planning, and control. Furthermore, we identify research gaps in the field, highlighting areas where further investigation is needed to advance the development and deployment of safe and efficient CAV systems.

Off-road autonomy validation presents unique challenges due to the unpredictable and dynamic nature of off-road environments. Traditional methods focusing on sequentially sweeping across the parameter space for variability analysis struggle to comprehensively assess the performance and safety of off-road autonomous systems within the imposed time constraints. This paper proposes leveraging scalable digital twin simulations within high-performance computing (HPC) clusters to address this challenge. By harnessing the computational power of HPC clusters, our approach aims to provide a scalable and efficient means to validate off-road autonomy algorithms, enabling rapid iteration and testing of autonomy algorithms under various conditions. We demonstrate the effectiveness of our framework through performance evaluations of the HPC cluster in terms of simulation parallelization and present the systematic variability analysis of a candidate off-road autonomy algorithm to identify potential vulnerabilities in the autonomy stack's perception, planning and control modules.

Shoppers look at clothes while others walk around Twelve Oaks Mall on Nov. 24, 2023, in Novi, Michigan. BUY SMARTER: Major retailers use AI to slash number of clothing returns when shopping online. UNPLUGGED FROM SOCIETY: Experts warn new tech could cause people to withdraw socially. TACTICAL TECH: Cheap drones can take out expensive military systems, warns former Air Force pilot pushing AI-enabled force. The military metaverse enables pilots to have more frequent training against relevant targets, Robinson said.

Abstract-- Robust feature matching forms the backbone for most Visual Simultaneous Localization and Mapping (vSLAM), visual odometry, 3D reconstruction, and Structure from Motion (SfM) algorithms. However, recovering feature matches from texture-poor scenes is a major challenge and still remains an open area of research. In this paper, we present a Stereo Visual Odometry (StereoVO) technique based on point and line features which uses a novel feature-matching mechanism based on an Attention Graph Neural Network that is designed to perform well even under adverse weather conditions such as fog, haze, rain, and snow, and dynamic lighting conditions such as nighttime illumination and glare scenarios. We perform experiments on multiple real and synthetic datasets to validate our method's ability to perform StereoVO under lowvisibility weather and lighting conditions through robust point and line matches. The results demonstrate that our method achieves more line feature matches than state-of-the-art linematching algorithms, which when complemented with point feature matches perform consistently well in adverse weather and dynamic lighting conditions.

We were able to complete the full integration of the Robotic Technology Kernel (RTK) into an electric vehicle by-wire system using lidar and GPS sensors. The solution included a mobile application to interface with the RTK-enabled autonomous vehicle. Altogether the system was designed to be modular, using the concepts of message-based software design that is built into the Robot Operating System (ROS), which is at the foundation of RTK. The team worked incrementally to develop working software to demonstrate each milestone on the path to successfully completing the RTK integration for the development of an application called the Vehicle Summoning System (VSS).

This ebook, based on the latest ZDNet / TechRepublic special feature, examines how driverless cars, trucks, semis, delivery vehicles, drones, and other UAVs are poised to unleash a new level of automation in the enterprise. Few technologies have been more anticipated heading into the 2020s than autonomous vehicles. Tantalizingly close and yet still perhaps decades from market adoption in some use cases, the technology is as promising as it is misunderstood. You've heard the consumer hype, but what gets less ink are the transformative changes that autonomous vehicles will bring -- in some cases already are bringing -- to the enterprise. Affecting sectors as disparate as shipping and logistics, energy, agriculture, transportation, construction, and infrastructure -- to name just a few -- it's hard to overstate the impact of the diverse and versatile set of technologies lumped into the decidedly broad category of'autonomous vehicles'. This guide will help you sort the hype from the business reality and tell you all you need to know about the autonomous vehicle revolution on the ground, in the air, and even at sea. In 1939, General Motors predicted we'd have an autonomous vehicle highway system up and running by the dawn of the 1960s. As with a lot of autonomous vehicle hype, that prediction was a tad premature, but it demonstrates the long history of autonomous vehicle development.

A test car operated by a robot crashes into a soft-sided electric vehicle. Robot-operated cars that smash into soft-sided sitting ducks, glass capillaries that help detect leaks and mountable gizmos for capturing data. Those are among the myriad testing methods on display at the recent Automotive Testing Expo in Novi, Mich. Testing for all sorts of things has always been important in the auto industry. But the advent of autonomous vehicles and advanced driver assist systems, or ADAS, has added both a new urgency and new challenges for companies who make their money conducting such tests, analyzing test data or creating testing systems.

Waymo, the autonomous vehicle company under Alphabet, has started creating 3D maps in some heavily trafficked sections of Los Angeles to better understand congestion there and determine if its self-driving vehicles would be a good fit in the city. For now, Waymo is bringing just three of its self-driving Chrysler Pacifica minivans to Los Angeles to map downtown and a section of Wilshire Boulevard known as Miracle Mile. Waymo employees will initially drive the vehicles to create 3D maps of the city. These maps are unlike Google Maps or Waze. Instead, they include topographical features such as lane merges, shared turn lanes and curb heights as well as road types and the distance and dimensions of the road itself, according to Waymo.

Stephen Crouch of Montana-based Blackmore explains how the company's lidar technology would help a robot car see what's ahead. Waymo will build self-driving vehicles in Detroit. The company, once known as Google's self-driving car project and now a leader in the push to develop autonomous vehicles, had previously said it was scouting locations in southeast Michigan but did not name a specific city. CEO John Krafcik revealed Detroit as the company's choice in a blog post Tuesday titled, "Making Waymos in Motor City." It refers to being "up and running" this year.

Alphabet Inc's self-driving car unit Waymo is expanding winter testing as it works to address a potential blind spot for autonomous vehicles: snowy and icy conditions. Waymo said Thursday that Michigan is the sixth state where the self-driving car project will test autonomous vehicles. The company chose the state to see how vehicles will respond in snow, sleet, and ice. Alphabet Inc's self-driving car unit Waymo is expanding winter testing as it works to address a potential blind spot for autonomous vehicles: snowy and icy conditions. Waymo has been testing vehicles in Texas, Arizona, Washington State, Nevada and California and starts in Michigan next week on public roads with a backup safety driver sitting in the driver seat if necessary.