Avancées en ingénierie automobile
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Modernization’s specificity of branded system of maintenance and repair during the transition to autonomous vehicles

Irina Makarova

Autonomous vehicles have become a logical outcome of the Intelligent Transport Systems direction’s implementation as a system strategy. This area, along with such as “artificial intelligence”, “robotics”, “electronics”, aimed at creating safe environment, is especially relevant for the Millennium Goals’ implementation . The article analyzes the directions of road vehicles intellectualization. The problems and ways to improve the safety, reliability and sustainability of transport systems are indicated. It is shown that to control the autonomous vehicles’ reliability it is necessary to improve the branded system of maintenance and repair. This is realized through the improvement of on-board diagnostic systems. The use of sensors that read data on the vehicle’s state, its routes and external factors affecting reliability ensure the adequacy and quality of the initial information. Using a single information space for generating operational databases as well as a defect codifier for generating failure statistics and their multidimensional analysis will allow us to determine the service strategy and also carry out its adjustment if necessary when changing the failure statistics. The article provides examples of the simulation models using to study the various factors’ influence on the maintenance and repair system. The results of a failure statistics multivariate analysis and methods for adjusting the service strategy based on it are presented. Early rhetoric surrounding self-driving cars set enormously high expectations. In 2011, Google X founder Sebastian Thrun wrote that Google’s early self-driving prototypes “drive anywhere a car can legally drive.” In 2012, Google released a video in which Steve Mahan, a visually impaired Californian, rode in the driver’s seat on an everyday journey through part of the San Francisco Bay Area, going through the drive-thru at a Taco Bell and picking up his dry cleaning; at the close of the video, Steve Mahan was designated as ”Self-Driving Car User #0000000001.” Later that year, at the signing of a California driverless car bill into law, Google founder Sergey Brin stated, “You can count on one hand the number of years until people, ordinary people, can experience this.” The 21st-century space race in technology had begun. Investments poured into self-driving startups, as automakers and Tier 1 suppliers mobilized large efforts to fend off Google and other potential disruptors. Israeli computer vision startup Mobileye had a highly successful initial public offering in July 2014 that valued the company at $5.31 billion. Thirty-two months later, Intel acquired Mobileye for $15.3 billion. In 2015, Elon Musk predicted that Tesla drivers would soon be able to safely go to sleep in their self-driving Teslas and wake up at their destinations. “In the past five years,” Alex Davies wrote in Wired in 2018, “autonomous driving has gone from ‘maybepossible’ to ‘definitely possible’ to ‘inevitable’ to ‘How did anyone ever think this wasn’t inevitable?’” Not everyone has agreed that a steering wheel free future was inescapable. Self-driving critics such as Steven Shladover of UC Berkeley pointed out challenges that continue to stymie researchers, such as coping with snow and ice and recognizing traffic cops and crossing guards. Robotics researchers, including Mary (Missy) Cummings from Duke University and Gill Pratt from Toyota Research Institute, pointed to the dangers that can occur when humans operate highly automated vehicles—such as failing to pay attention when intervention is necessary. Roboticists such as MIT’s Rodney Brooks pointed out that recent advances in detecting objects in images does not imply that general-purpose artificial intelligence is close at hand. Co-author David A. Mindell wrote in 2015 that an immediate leap to full autonomy was a less meaningful problem than solving for ideal mixes of human and machine.