Can an Engineering Competition Catalyze Curriculum Innovation?

This article describes the ongoing efforts of a multidisciplinary group of faculty at an undergraduate institution to form a team and compete in the IBM AI XPRIZE competition. We describe the advantages and disadvantages of faculty participation in major engineering competitions over more traditional professional activities at undergraduate engineering institutions. Our discussion is focused on the benefits to three major groups: undergraduate students, faculty, and academic institutions. We use examples from our one year of experience in the competition and from the literature to illustrate these benefits. Already observed benefits from the competition include increased student engagement, development and introduction of a new minor in cognitive science, the purchase of a state-of-the-art robot and a deep learning server, enhanced multidisciplinary collaboration among faculty, and heightened awareness among administrators of the growing importance of artificial intelligence (AI) technologies. Results of a student survey regarding their involvement in with the team are presented

Artificial Intelligence: Uses and Misuses

Artificial Intelligence (AI) was mostly regarded as science-fiction in the past but with the recent advancements in technology, it has silently crept into our lives. From social media to computer games to self-driving cars to military gadgets to personal digital assistants, AI is everywhere. This progress is also due to a paradigm shift in AI community where current trend is to make AI stronger in specific domains rather than making a human-like AI which can do anything. Resultantly, AI can now out-perform humans in many areas. But this progress of AI is scary for some people who are predicting the 201C;rise of machines201D; in half a century or so if AI progress remains unbridled

A local trajectory planning and control method for autonomous vehicles based on the RRT algorithm

This paper presents a local trajectory planning and control method based on the Rapidly-exploring Random Tree algorithm for autonomous racing vehicles. The paper aims to provide an algorithm allowing to compute the planned trajectory in an unknown environment, structured with non-crossable obstacles, such as traffic cones. The investigated method exploits a perception pipeline to sense the surrounding environment by means of a LIDAR-based sensor and a high-performance Graphic Processing Unit. The considered vehicle is a four-wheel drive electric racing prototype, which is modeled as a 3 Degree-of-Freedom bicycle model. A Stanley controller for both lateral and longitudinal vehicle dynamics is designed to perform the path tracking task. The performance of the proposed method is evaluated in simulation using real data recorded by on-board perception sensors. The algorithm can successfully compute a feasible trajectory in different driving scenarios

The Black Box Solution to Autonomous Liability

Autonomous vehicles, or self-driving cars, have the potential to revolutionize modern transportation through increased productivity and safety. Today, industry leaders in both automotive manufacturing and technology development are engaged in the design and production of these vehicles. Representatives from these companies have already successfully lobbied a number of state legislatures to permit the testing and use of autonomous vehicles. While the prospect of a mass market in autonomous vehicles is exciting for both consumers and manufacturers, the use of autonomous vehicles implicates novel legal issues. For example, when a car drives itself, who is responsible when it crashes? Should the manufacturer who designed the car be held liable? Or the driver who directs the vehicle? To solve this problem, I argue that all autonomous vehicles should be required to carry an Event Data Recorder (“EDR”) to monitor and record data about vehicle functioning. This technology is analogous to the Flight Data Recorder (“FDR”), colloquially known as a “black box,” found on airplanes. An FDR records and transmits information about an airplane’s functionality, and this information helps investigators determine whether the cause of a crash was human error or mechanical failure. The same would apply to autonomous vehicles. Given the possibility of manufacturers being held liable for vehicle crashes, the use of EDR data would limit financial liability in tort claims and increase manufacturer willingness to develop autonomous technology. However, as with the collection of any personal data, there are privacy issues that must also be examined. EDRs contain personal data, such as the geographic location of the vehicle and the owner’s driving patterns. The collection and use of this data may be harmful to an individual’s privacy rights. As a result, I argue that vehicle owners should be considered the sole owner of EDR data and no data may be shared for any commercial purpose without affirmative consent of the owner

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Mobility in China: a conceptual take on a personal vehicle for China in 2020 that enhances maneuverability

China has the largest projected automobile market in the world, expected to surpass the United States as the largest car market in the world by 2025. The combination of large population, a mass movement of citizens to cities, and a pollution crisis creates unique opportunities in China for automobile design. The first generations of Chinese to embrace the automobile have been attracted to them by the same values that have been embraced by the West such as prestige, a reflection of personal success, and a sense of freedom of movement. This attraction has given rise to traditional brands such as Buick, Audi and Mercedes Benz. However, as a new generation matures aware of China\u27s problems presented by a growing number of automobiles, a shift is happening. Awareness of ecological issues, as well as an acute sense of forthcoming issues with traffic density inside and surrounding China\u27s vast metropolises, suggests future generations are more willing to embrace alternative solutions. China has a young automotive identity, currently relating to aesthetic qualities of certain brands. Without the same historical narrative that has informed the rise of the car in the West, China is poised to create one that can respond more acutely to its needs. With fossil fuels the source of many potential problems in both pollution and cost of use, alternative energy vehicles will likely form the backbone of future growth of the automobile in China. Currently Toyota, GM, BMW, and Audi, to name a few, are actively pursuing alternative energy power plant designs. By 2020, alternative energy vehicles will make up a significant percentage of new vehicle sales in the Western world. Potential solutions come in the form of gas and diesel hybrids, all electric, hydrogen fuel cell and Hydrogen internal combustion engines. For a car to successfully meet the needs of Chinese consumers, it will need to be both ecologically friendly and highly maneuverable to maximize use of the limited space available on congested streets. The simple act of making a U-turn on a narrow street in a conventional four-wheeled vehicle can cause traffic jams. Additionally, automation in future thoroughfares can reduce the space between individual automobiles, effectively placing more vehicles in less space. This thesis establishes the need for rethinking the physical footprint of the automobile in the context of the Chinese market and provides a framework for a new vehicle design