The problem of allocating accident responsibility for autonomous driving is a difficult issue in the field of autonomous driving. Due to the complexity of autonomous driving technology, most of the research on the responsibility of autonomous driving accidents has remained at the theoretical level. When encountering actual autonomous driving accidents, a proven and fair solution is needed. To address this problem, this study proposes a multi-subject responsibility allocation optimization method based on the RCModel (Risk Chain Model), which analyzes the responsibility of each actor from a technical perspective and promotes a more reasonable and fair allocation of responsibility.

The last decade has seen companies operating under increasing levels of disruption. Quickly changing customer preferences, as well as demand uncertainty and disruptions, are challenging planning systems to unprecedented degrees. National security interests, trade barriers, and logistics disruptions are pushing businesses to find alternatives to globalized supply chains. Major swings in demand are calling for drastic operational and capital cost reduction in some areas and rapid growth in others. Physical distancing and remote work are forcing manufacturers to reconfigure manufacturing flows and management.

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.

Transportation of people, goods, and tools is an automobile's basic or primary function. However, this premise was not sustainable or rather good enough for marketing automobiles to its customers -- it had to be extended. During the last century, automobile manufacturers or OEMs (original equipment manufacturers) captured the imagination of their customers by tapping into their emotions and aspirations -- based on owning vehicles that were fast, well designed, and perhaps status symbols. Some of these vehicles remain as aspirational as they were earlier to this day, only to create a legacy of their own and become costlier with time. Consider a Ford Mustang 1967, for example. This fundamental ground on which the manufacturers thrived, wherein cars became a tad faster with more horsepower added to them, had external design changes, or became luxurious with different interior options, was not disruptive.

The European Automobile Manufacturers' Association (ACEA) welcomes the European Commission's initiative on Artificial Intelligence and its goal to develop a European ecosystem of excellence and trust around AI. ACEA supports the risk-based approach laid down in the proposal. Indeed, a key element for a successful European approach to AI is that the requirements set out in the Regulation are proportionate to the risk level of the AI applications, and are not too burdensome for businesses across Europe, as this would restrain innovation and hinder AI adoption. A coherent legal framework is crucial for accelerating AI deployment in motor vehicles. We stress our support to the sectoral approach taken by the Commission, as this will ensure that automotive products remain regulated primarily through their sector-specific framework. In order to avoid duplicating the existing governance mechanisms, ex ante conformity assessment procedures, and the monitoring and market surveillance in place for motor vehicles and their safety components, it is essential that the technical requirements for automotive products are integrated into the existing vehicle type-approval framework.

S. Somasegar is managing director at Madrona Venture Group and the former head of Microsoft's Developer Division. Daniel Li is an investor with Madrona Venture Group. "The technology is essentially here… We have machines that can make a bunch of quick decisions that could drastically reduce traffic fatalities, drastically improve the efficiency of our transportation grid, and help solve things like carbon emissions that are causing the warming of the planet." Interestingly, this statement didn't come from a futurist like Elon Musk or Mark Zuckerberg or Jeff Bezos; this was President Obama discussing autonomous vehicles in an interview with WIRED last fall. Over the last year, we have seen many groundbreaking announcements regarding autonomous cars, from companies like Ford promoting its autonomous vehicle leader to the position of CEO, to Tesla's NHSTA investigation showing a 40 percent decrease in accidents with Autopilot enabled and Audi beginning mass-market sales of a "Level 3" autonomous car. Nevertheless, many questions in the world of autonomous vehicles remain unanswered.

With more than 250 automotive companies in the state, from Lear, Kemet, Koyo, to Michelin, BMW, and Bridgestone (to name a few), it is little wonder that South Carolina is ranked #3 in automotive manufacturing strength for many reasons. South Carolina is one of leading locations for vision guided vehicles (VGV), driven in part to the increasing North American fork truck free (FTF) initiatives. Encouraging public-private partnerships, which serve as the foundation for research in South Carolina, world-class brands like BMW and Michelin are partnering with universities to bring collaboration to the next level. At facilities across the state, researchers driven by the needs of the automotive industry work with students, multi-disciplinary faculty members, and industry partners to determine the next generation automation technologies. The automotive sector often requires a proof-of-concept success story in one location before the technology solution is implemented enterprise-wide.