Editing Emerging Research Directions (section)

===4. Emerging Use Cases in Vehicle Edge Computing===
'''4.1. Autonomous Driving'''
Autonomous vehicles (AVs) rely on edge computing to interpret sensor data in real-time. They use:
*LIDAR and radar for object detection.
*Local AI models for path planning.
*Embedded GPUs/ASICs for deep learning inference.

Edge computing enables AVs to operate safely without full cloud reliance. 

[[File: Autonomous vehicle sensor data.jpg|600px|thumb|center| ''Figure 5: [Autonomous vehicle sensor data. Source: Seminar Presentation.]'']]

'''4.2. Predictive Maintenance'''
In the user's final project [2][4], edge computing was leveraged for preventive maintenance warnings using AI models deployed on vehicle microcontrollers. These systems:
Monitor signals from OBD-II interfaces.
Detect early anomalies in vibration, RPM, or temperature.
Alert drivers before mechanical failures occur.
This showcases how edge AI can extend vehicle life and improve safety.

[[File: Predictive Maintenance.jpg|600px|thumb|center| ''Figure 6: [Predictive Maintenance.jpg[4]]'']]

'''4.3. Smart Infrastructure Monitoring'''
Connected vehicles can serve as mobile sensors for cities. Shi [5] highlights how they:
Detect potholes or damaged infrastructure.
Share data with city edge servers for repair scheduling.
Enable crowdsourced infrastructure diagnostics.
This creates a synergistic ecosystem between vehicles and smart cities.

'''4.4. Safety and Law Enforcement'''
Edge-powered applications include:
Real-time passenger behavior monitoring for ride-sharing.
Situational awareness for police vehicles using AI and camera feeds
Edge-assisted bodycam processing for faster incident reporting.


[[File:The future of connected and autonomous vehicle.jpg|600px|thumb|center| ''Figure 7: [The future of connected and autonomous vehicle - Source: Seminar Presentation.]'']]