Driverless Vehicles: The Socioeconomic Effects and Future of Autonomous Technology


Note: This article was originally published as an essay for an ethics class. I have decided to keep it in the same format given the sources used. It was originally written sometime in 2015. Quite interesting to observe similarities, differences, and growth in just the short span of 3 years when it comes to driverless cars and autonomous technology. Thousands of years ago, humans roamed the earth on nothing but bare feet. Hundreds of years ago, we rode horses and carriages, even trains. Then, Henry Ford and the automobile came along to pave the way for one of today's most common ways of transportation.

Still Waiting for Self-Driving Cars

Communications of the ACM

Over the past decade, technology and automotive pundits have predicted the "imminent" arrival of fully autonomous vehicles that can drive on public roads without any active monitoring or input from a human driver. Elon Musk has predicted his company Tesla would deliver fully autonomous vehicles by the end of 2021, but he made similar predictions in 2020, 2019, and 2017. Each prediction has fallen flat, largely due to real-world safety concerns, particularly related to how self-driving cars perform in adverse conditions or situations. Despite such proclamations from Tesla, which released its optimistically named Full Self Driving capability for AutoPilot in October 2021, fully automated self-driving cars have not yet arrived. Instead, most manufacturers are offering systems that feature capabilities that generally fall within the first three of the six levels of autonomy defined by the Society of Automotive Engineering (SAE), which range from Level 0 (no driving automation) to Level 5 (full self-driving capabilities under all conditions).

The Self-Driving Car: Crossroads at the Bleeding Edge of Artificial Intelligence and Law Artificial Intelligence

Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.

Roadmap for Cybersecurity in Autonomous Vehicles Artificial Intelligence

Autonomous vehicles are on the horizon and will be transforming transportation safety and comfort. These vehicles will be connected to various external systems and utilize advanced embedded systems to perceive their environment and make intelligent decisions. However, this increased connectivity makes these vehicles vulnerable to various cyber-attacks that can have catastrophic effects. Attacks on automotive systems are already on the rise in today's vehicles and are expected to become more commonplace in future autonomous vehicles. Thus, there is a need to strengthen cybersecurity in future autonomous vehicles. In this article, we discuss major automotive cyber-attacks over the past decade and present state-of-the-art solutions that leverage artificial intelligence (AI). We propose a roadmap towards building secure autonomous vehicles and highlight key open challenges that need to be addressed.

My Top 5 Predictions for AI in 2022


Ethics is at the center of AI research more than ever. We have a better understanding of the risks of harm language models entail -- companies keep improving language models making them not just bigger but smarter and more efficient, multimodal systems are more common (e.g. Google's MUM and OpenAI's DALL·E), and real-world AI is taking leaps forward -- and backward. All in all, AI has maintained or even accelerated the pace of progress we've seen throughout the last decade. The AI community will bring new promising developments and impressive breakthroughs, some of which we can foresee.

Challenges of Artificial Intelligence -- From Machine Learning and Computer Vision to Emotional Intelligence Artificial Intelligence

Artificial intelligence (AI) has become a part of everyday conversation and our lives. It is considered as the new electricity that is revolutionizing the world. AI is heavily invested in both industry and academy. However, there is also a lot of hype in the current AI debate. AI based on so-called deep learning has achieved impressive results in many problems, but its limits are already visible. AI has been under research since the 1940s, and the industry has seen many ups and downs due to over-expectations and related disappointments that have followed. The purpose of this book is to give a realistic picture of AI, its history, its potential and limitations. We believe that AI is a helper, not a ruler of humans. We begin by describing what AI is and how it has evolved over the decades. After fundamentals, we explain the importance of massive data for the current mainstream of artificial intelligence. The most common representations for AI, methods, and machine learning are covered. In addition, the main application areas are introduced. Computer vision has been central to the development of AI. The book provides a general introduction to computer vision, and includes an exposure to the results and applications of our own research. Emotions are central to human intelligence, but little use has been made in AI. We present the basics of emotional intelligence and our own research on the topic. We discuss super-intelligence that transcends human understanding, explaining why such achievement seems impossible on the basis of present knowledge,and how AI could be improved. Finally, a summary is made of the current state of AI and what to do in the future. In the appendix, we look at the development of AI education, especially from the perspective of contents at our own university.

Japan Is Implementing Self-Driving Tech Into Most Vehicles By 2022


Japan has started to bolster its pace in the automated driving sector with the likes of well-known brands, including Mazda, Toyota, and even Lexus implementing the technology into their vehicles. Since September, as reported via Bloomberg, Japan has slowly begun integrating self-driving cars to suit rural areas and the elderly better. By 2022, several automobile manufacturers will seek to invite level 2-based self-driving mechanics to their vehicles to assist the country's overall endeavors. There are a total of five main levels of automated driving technology for self-driving cars. At the fifth level, the automobile is fully automated and drives itself.

Autonomous delivery picking up in US


Autonomous vehicles (AV) play an increasingly important role in food and parcel deliveries. In early December, Silicon Valley-based startup Nuro announced that it was launching the first commercial autonomous delivery in California. Partnering with 7-Eleven, the company provides the service for residents of Mountain View, where the business is located. According to a blog post from Nuro's co-founder Zhu Jiajun, customers can access the autonomous delivery through 7-Eleven's 7NOW delivery app. Nuro currently offers the service with its Prius vehicles in fully autonomous mode, expecting to replace them with its R2 autonomous cars later.

Artificial Intellgence -- Application in Life Sciences and Beyond. The Upper Rhine Artificial Intelligence Symposium UR-AI 2021 Artificial Intelligence

The TriRhenaTech alliance presents the accepted papers of the 'Upper-Rhine Artificial Intelligence Symposium' held on October 27th 2021 in Kaiserslautern, Germany. Topics of the conference are applications of Artificial Intellgence in life sciences, intelligent systems, industry 4.0, mobility and others. The TriRhenaTech alliance is a network of universities in the Upper-Rhine Trinational Metropolitan Region comprising of the German universities of applied sciences in Furtwangen, Kaiserslautern, Karlsruhe, Offenburg and Trier, the Baden-Wuerttemberg Cooperative State University Loerrach, the French university network Alsace Tech (comprised of 14 'grandes \'ecoles' in the fields of engineering, architecture and management) and the University of Applied Sciences and Arts Northwestern Switzerland. The alliance's common goal is to reinforce the transfer of knowledge, research, and technology, as well as the cross-border mobility of students.

How Deep Learning has completely changed the entire self-driving car industry 🚗


So now, without any further ado let's dive into the how the self-driving car industry looked like 15–20 years ago! Unless you are living under a rock, you probably know that the self-driving car industry has become one of the hottest industries in the last 5–10 years. Some of the world's biggest companies like Google, Tesla, GM are working on self-driving cars. These companies have spent more than 120 billion dollars on self-driving car R&D just in 2020 alone! The CEOs of these companies are saying that they are on the verge of creating the driverless cars that we all imagine when we think about our future cities(the ones where you can just fall asleep in to get the extra hour of sleep).