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Using TypeDB for Autonomous Vehicles

The team at TNO set out to combine knowledge of human driving — e.g. ethics, laws, norms, preferences, common environmental understanding — with the technological side of autonomous driving — e.g. control theory, data-driven AI, black-box algorithms (DNN), and end-to-end learning. To achieve this and be able to reason over the collective knowledge, they used TypeDB.

An example where the combination of this knowledge demonstrates its value is when hardcoded knowledge meets contextual knowledge of a given situation in time. Let’s say an autonomous vehicle is programmed to drive slower in school zones, obviously needed for safety, but what about when the school is closed? Does the vehicle still need to drive at a slower speed?

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Checklist for Thinking About Cybersecurity in Connected Vehicles

A comprehensive approach to security is essential for the protection of connected vehicle systems. This article presents a set of security recommendations based on analyzing security risks for each step in developing and deploying AI and other connectivity systems in autonomous vehicles.

The recommendations are intended to be used as a roadmap by vehicle manufacturers, system integrators, suppliers, and other stakeholders to ensure that an end-to-end approach to security is applied throughout the lifecycle of AI components.

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The Last Mile: How the Pandemic Revealed New Applications of Autonomous Vehicles

The Acceleration of Autonomous Vehicle Applications Due to COVID-19

Hint: It’s not focused on personal transportation.

Autonomous vehicles have long been a mainstay of both outlandish fiction and legitimate research, in some interpretations predating the invention of the car itself. By some accounts, full self-driving capabilities would be a major boon in terms of safety and promises to narrow wealth-based gaps in good access to transportation. Perhaps the impact could be as significant as the past adoption of safety bicycles, which expanded the distance one could travel by 3 to 4 times or more, without incurring the costs of owning a horse (or car) and the facilities to care for them.

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Deep Reinforced Learning: Addressing Complex Enterprise Challenges

Current deep learning algorithms and methods are nowhere near the holy grail of “Artificial General Intelligence (AGI).”

Current algorithms lean more towards narrow learning, meaning they are good at learning and solving specific types of problems under specific conditions. These algorithms take a humongous amount of data as compared to humans who can learn from relatively few learning encounters. The transfer process of these learnings from one problem domain to another domain is somewhat limited as well.