Embedded Control Strategies for Autonomous Electric Vehicles in Smart Cities

Abstract

Autonomous electric vehicles (AEVs) are revolutionizing the transportation sector by integrating embedded electrical systems with automation technologies. This paper presents a comprehensive study on embedded controllers for power management, path planning, and autonomous decisionmaking in AEVs operating within smart city infrastructures. Embedded microcontrollers are designed to optimize battery usage, regenerative braking, and efficient charging cycles. Additionally, embedded artificial intelligence algorithms play a pivotal role in enhancing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, thereby improving traffic flow and reducing congestion. The research also investigates the integration of real-time operating systems (RTOS) with sensor fusion techniques, enabling vehicles to perceive and react swiftly to dynamic road conditions. Challenges such as cybersecurity, low-latency communication, and energy-efficient hardware design are also discussed. The study highlights case scenarios where embedded automation systems contributed to safer and more reliable autonomous navigation in urban environments, ultimately promoting sustainable transportation.

KEYWORDS: Autonomous Vehicles, Embedded Control, Smart Cities, Sensor Fusion, Power Management