Editing Emerging Research Directions (section)

== 7.3 Vehicle Computing ==

===Introduction===
The rapid advancement in intelligent transportation systems (ITS), autonomous driving, and vehicle-to-everything (V2X) communication has made edge computing an indispensable enabler of innovation. As vehicles become increasingly autonomous, the need for fast, reliable, and low-latency processing grows. Edge computing, by decentralizing data processing and moving it closer to the data source, is revolutionizing how vehicles interact with their environment.
This chapter explores the growing field of vehicle edge computing (VEC), analyzing its current role, challenges, and emerging use cases. We synthesize insights from industry presentations, academic research, and real-world implementations to provide a cohesive narrative of the transformative impact of edge computing on the transportation ecosystem.

===1. Overview of Edge Computing===
Edge computing refers to the deployment of computing services closer to data sources such as sensors, actuators, or connected devices. Unlike traditional cloud computing, which relies on remote data centers, edge computing reduces latency, saves bandwidth, and enhances real-time decision-making capabilities.

'''Key Benefits for Automotive Applications:'''
*Ultra-low latency: Enables real-time decision-making for safety-critical functions.
*Bandwidth optimization: Reduces data transmission by processing information locally.
*Improved privacy and security: Minimizes data exposure by localizing computation.
*Increased reliability: Maintains operation in areas with intermittent connectivity.

[[File:Proposed Future Vehicle Computing Paradigm.jpg|600px|thumb|center| ''Figure 1: [Proposed Future Vehicle Computing Paradigm.[5].]'']]

===2. The Need for Edge in Vehicles===

'''2.1. Data Explosion in Connected Vehicles'''
Modern vehicles are equipped with over 300 sensors and generate terabytes of data daily. As noted by Shi et al. [5], a connected vehicle can produce up to 35 TB of data per day. Centralized cloud architectures are insufficient to handle such high-throughput data streams efficiently.

[[File:computer_on_wheels.jpg|600px|thumb|center| ''Figure 2: [Computer on Wheels or Moving Data Center. Source: Seminar Presentation.]'']]


'''2.2. Latency and Safety'''
Latency is critical in autonomous systems. In traditional architectures, a vehicle may need to send sensor data to the cloud, await processing, and receive instructions—a delay that could be fatal in scenarios like pedestrian detection.
"By the time the information has traveled to and from the server, the accident has already occurred."
Edge computing enables immediate action, reducing the delay to milliseconds.

'''2.3. Software-Defined Vehicles'''
The vehicle industry is moving towards software-defined vehicles (SDVs), which require OTA (Over-The-Air) updates, real-time diagnostics, and modular software services. Edge computing facilitates dynamic service delivery, AI inference, and adaptive updates without overwhelming the cloud infrastructure.

[[File:Disruptive transformation of automotive mobility.jpg|600px|thumb|center| ''Figure 3: [Disruptive transformation of automotive mobility[5].]'']]

===3. Architectures for Vehicle Edge Computing===

'''3.1. Multi-Tiered Architecture'''
Vehicle edge computing generally involves a four-layer architecture:
*On-board Vehicle Computing (VEC): Direct AI inference and sensor processing.
*Edge Servers (e.g., RSUs, roadside units): Shared compute resources at intersections or base stations.
*IoT Layer: Includes environmental sensors and auxiliary connected devices.
*Cloud Tier: For large-scale analytics, simulations, and HD map updates.

'''3.2. Real-World Example: Autonomous Vehicle Stack'''

Autonomous driving systems like those developed by Waymo and Uber use a distributed computing model:
*Local x86 nodes with GPUs and FPGAs on the vehicle.
*Sensors (LIDAR, radar, cameras) feed into real-time AI models.
*Local control software performs path planning and object avoidance.
[[File:seamless autonomous mobility.jpg|600px|thumb|center]]
[[File:self-driving vehicle basics.jpg|600px|thumb|center| ''Figure 4: [Vehicle Stack from Uber AV System – Custom Compute Nodes, Sensor Interfaces, Telematics Modules. Source: Seminar Presentation.]'']]

===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.]'']]

===5. Technical Challenges and Research Directions===
'''5.1. Heterogeneity and Resource Constraints'''
Vehicles use different CPUs, GPUs, and network modules. Developing uniform SDKs and runtime environments (like the VPI mentioned by Shi[5]) is crucial.

'''5.2. Real-Time OS and Scheduling'''
Due to safety requirements, OS-level guarantees are needed for tasks like braking, detection, or communication. Time-sensitive networking and real-time scheduling are active research areas.

'''5.3. Data Offloading and Bandwidth Optimization'''
Not all data can be offloaded to the cloud. Techniques like compressed AI inference, model partitioning, and log prioritization are necessary to reduce bandwidth costs.

'''5.4. Security and Privacy'''
With increased vehicle connectivity comes increased risk. Secure boot, sandboxing, trusted execution environments, and decentralized authentication are vital in VEC systems.

'''5.5. Standardization and APIs'''
There is a need for standardized vehicle-to-cloud and vehicle-to-infrastructure APIs to enable third-party service integration.

[[File: different players in automated driving.jpg|600px|thumb|center| ''Figure 8: [Different Players in Automated Driving: Seminar Presentation.]'']]

===6. Future Vision and Business Implications===

'''6.1. Vehicles as Edge Platforms'''
Just like smartphones, vehicles will host apps that:
Provide ride-sharing capabilities.
Monitor infrastructure.
Run entertainment or diagnostic services.
OEMs could open SDKs for 3rd-party app development, creating a new vehicle app economy.

'''6.2. Distributed AI at the Edge'''
Future systems will involve collaborative intelligence, where vehicles communicate with other vehicles and infrastructure to jointly solve navigation, perception, and safety problems.

'''6.3. Greener Cities and Sustainable Transport'''
Edge-driven predictive maintenance, optimized routing, and dynamic parking can reduce fuel usage and improve traffic flow, supporting sustainability goals.
''Figure 3: [Four-Tier Architecture of Vehicle Computing – Source: Shi et al. [5]]''

===Conclusion===
Edge computing is set to redefine the automotive landscape. By enabling real-time, decentralized intelligence, it supports safer, smarter, and more efficient transportation. From predictive maintenance and autonomous navigation to infrastructure monitoring and V2X communication, edge computing is the foundation of tomorrow’s intelligent mobility ecosystem.
As research and development continue, addressing challenges such as heterogeneity, security, and scalability will be essential. The vehicle edge computing paradigm not only presents technical innovation but also opens doors for new business models, societal benefits, and academic exploration.


=== References ===
[1] S. Mohammad, M. A. A. Masuri, S. Salim and M. R. A. Razak, "Development of IoT Based Logistic Vehicle Maintenance System," 2021 IEEE 17th International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia, 2021, pp. 127-132, doi: 10.1109/CSPA52141.2021.9377290.

[2] R. Rayhana et al., "Distributed Predictive Maintenance Through Edge Computing," 2024 IEEE 22nd International Conference on Industrial Informatics (INDIN), Beijing, China, 2024, pp. 1-6, doi: 10.1109/INDIN58382.2024.10774467.

[3] P. Bansal, "An Artificial Intelligence Framework for Estimating the Cost and Duration of Autonomous Electric Vehicle Maintenance," 2022 International Conference on Edge Computing and Applications (ICECAA), Tamilnadu, India, 2022, pp. 851-855, doi: 10.1109/ICECAA55415.2022.9936279.

[4] Mohamed Aboulsaad, "Preventive Maintenance Warning in Vehicles by Using AI," Final Project, 2024.

[5] Weisong Shi et al., "Vehicle Computing: Vision and Challenges," 2023.