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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Global Positioning System (GPS) technology has revolutionized navigation and positioning, but its accuracy is often compromised by various errors. These errors, stemming from environmental, satellite, and receiver-related factors, require careful mitigation to ensure reliable performance across applications.Atmospheric ErrorsGPS signals travel through the Earth’s ionosphere and troposphere, introducing delays which affect accuracy. The ionosphere is strongly influenced by charged particles,...
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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
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When AI takes the wheel: AI-defined vehicles principles and pitfalls.

Marco De Vincenzi1, Chiara Bodei2, Ilaria Matteucci1

  • 1Institute of Informatics and Telematics - CNR, Pisa, Italy.

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Summary

AI-defined vehicles (AIDVs) represent a new era of intelligent machines, evolving beyond traditional cars. This research conceptualizes AIDVs, highlighting their adaptive AI capabilities and integration into intelligent transportation systems.

Keywords:
artificial intelligence (AI)autonomous vehicle (AV)intelligent transportation system (ITS)safetysecurityvehicle-to-everything (V2X)

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Area of Science:

  • Robotics and Artificial Intelligence
  • Intelligent Transportation Systems

Background:

  • Modern vehicles are evolving into sophisticated robotic systems with autonomous capabilities.
  • Artificial Intelligence (AI) is driving a new class of vehicles: AI-defined vehicles (AIDVs).

Purpose of the Study:

  • To introduce and conceptualize AI-defined vehicles (AIDVs).
  • To analyze the defining principles, capabilities, and challenges of AIDVs.
  • To distinguish AIDVs from existing vehicle classes.

Main Methods:

  • Conceptual analysis of AI-defined vehicles.
  • Identification of risks associated with adaptive AI in vehicles.
  • Development of a preliminary roadmap for integrating AIDVs into Intelligent Transportation Systems (ITS).

Main Results:

  • AIDVs embed interaction, adaptability, sustainability, and ethical governance as core design principles.
  • Adaptive AI introduces new risks that require careful management.
  • A roadmap for integrating AIDVs into ITS is proposed.

Conclusions:

  • AIDVs represent a significant evolution in vehicle technology, moving beyond traditional autonomous driving.
  • Addressing the risks of adaptive AI is crucial for the successful and ethical integration of AIDVs into future transportation networks.