Reliable lane-following algorithms are essential for safe and effective autonomous driving. This project was primarily focused on developing and evaluating different lane-following programs to find the most reliable algorithm for a Vehicle to Everything (V2X) project. The algorithms were first tested on a simulator and then with real vehicles equipped with a drive-by-wire system using ROS (Robot Operating System). Their performance was assessed through reliability, comfort, speed, and adaptability metrics. The results show that the two most reliable approaches detect both lane lines and use unsupervised learning to separate them. These approaches proved to be robust in various driving scenarios, making them suitable candidates for integration into the V2X project.

Self-driving vehicles have the potential to reduce accidents and fatalities on the road. Many production vehicles already come equipped with basic self-driving capabilities, but have trouble following lanes in adverse lighting and weather conditions. Therefore, we develop, analyze, and evaluate two vehicular lane-keeping algorithms under dynamic weather conditions using a combined deep learning- and hand-crafted approach and an end-to-end deep learning approach. We use image segmentation- and linear-regression based deep learning to drive the vehicle toward the center of the lane, measuring the amount of laps completed, average speed, and average steering error per lap. Our hybrid model completes more laps than our end-to-end deep learning model. In the future, we are interested in combining our algorithms to form one cohesive approach to lane-following.

In this work, we study the problem of common and unique feature extraction from noisy data. When we have N observation matrices from N different and associated sources corrupted by sparse and potentially gross noise, can we recover the common and unique components from these noisy observations? This is a challenging task as the number of parameters to estimate is approximately thrice the number of observations. Despite the difficulty, we propose an intuitive alternating minimization algorithm called triple component matrix factorization (TCMF) to recover the three components exactly. TCMF is distinguished from existing works in literature thanks to two salient features. First, TCMF is a principled method to separate the three components given noisy observations provably. Second, the bulk of the computation in TCMF can be distributed. On the technical side, we formulate the problem as a constrained nonconvex nonsmooth optimization problem. Despite the intricate nature of the problem, we provide a Taylor series characterization of its solution by solving the corresponding Karush-Kuhn-Tucker conditions. Using this characterization, we can show that the alternating minimization algorithm makes significant progress at each iteration and converges into the ground truth at a linear rate. Numerical experiments in video segmentation and anomaly detection highlight the superior feature extraction abilities of TCMF.

Chaganti, R., Vinayakumar, R., Alazab, M., and Pham, T.D. Stegomalware: A Systematic Survey of Malware Hiding and Detection in Images, Machine Learning Models and Research Challenges, Cornell University, October 6, 2021.

Robotics simulation provides many advantages during the development of an intelligent ground vehicle (IGV) such as testing the software components in varying scenarios without requiring a complete physical robot. This paper discusses a 3D simulation environment created using rapid application development and the Unity game engine to enable testing during a mobile robotics competition. Our experience shows that the simulation environment contributed greatly to the development of software for the competition. The simulator also contributed to the hardware development of the robot. INTRODUCTION Simulations have been a major part of robotics research and development for decades.

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).

The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...

"The language I've invented is pronounced with the same phonetics as Latin," explained Justin Harlan, my 21-year-old student. He was doing a presentation on his video game Ordenai, which was so outstanding that it left my boisterous class speechless. This was in the fall of 2019, my first semester teaching Creative Writing for Video Gamers at Lawrence Technological University (LTU) in Southfield, Michigan. This was a class I created, with the help of other faculty, and a prerequisite for those majoring in video game design. Awestruck at the scope of Harlan's game, I noticed several elements readily found in classic literature that were intimately woven into his story.

The car industry is reinventing the wheel to prepare for autonomous vehicles. Sumitomo Rubber Industries Ltd., whose roots stretch back to when Henry Ford was building his Model T, is developing a "smart tire" that can monitor its own air pressure and temperature, and eventually respond by itself to changes in road conditions. Yet it's more than just tires that are being changed. Koito Manufacturing Co., AGC Inc. and Lear Corp. are putting semiconductors and sensors inside headlights, glass and seats to make them as intelligent as the self-driving cars. Alphabet Inc.'s Waymo LLC, Intel Corp.'s Mobileye NV and Baidu Inc. dominate the core technology for autonomous driving, yet suppliers still count on finding their own space in the business.

The April DJUG meeting will be at Lawrence Technological University in Southfield, Michigan. We have reserved the Mary E. Marburger Science and Engineering Auditorium (room S100) so enter the Science Building and look for room S100. In the age of quantum computing, computer chip implants and artificial intelligence, it's easy to feel left behind. For example, the term "machine learning" is increasingly bandied about in corporate settings and cocktail parties, but what is it, really? In this session, James Weaver will give a gentle introduction to machine learning topics such as supervised learning, unsupervised learning, reinforcement learning, and deep learning.