5G technology represents the fifth generation of wireless communication networks, offering significant advancements over its predecessors. It introduces higher speeds, lower latency, greater capacity, and improved reliability, opening up new possibilities for various industries, including the Automotive Industry.
Some specific areas where 5G can make a big impact is as follows :
Enhanced Vehicle Connectivity: 5G would enable faster and more reliable communication between vehicles, infrastructure, and the surrounding environment. Will highlight the benefits of enhanced vehicle connectivity, such as improved safety, real-time data exchange, and optimized traffic management. The potential future use cases can include vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to enable advanced features like autonomous driving and cooperative collision avoidance in controlled environment
Intelligent Transportation Systems (ITS): 5G technology enables the development of Intelligent Transportation Systems. 5G will ensure efficient traffic management, smart traffic lights, and real-time navigation systems.
Over-the-Air (OTA) Updates and Remote Diagnostics : 5G can enable efficient OTA software updates, eliminating the need for physical recalls and reducing maintenance costs. The remote diagnostics using 5G connectivity can improve vehicle performance and reduce downtime.
V2X and Autonomous Driving
Autonomous vehicles requires constant streaming of sensor data to support complex decision-making algorithms for handling everchanging dynamic roadway conditions very quickly . In the standards derived from the society of automotive engineers (SAE), autonomous vehicles can be grouped into six different levels with incremental automation levels, namely the Level of Automation (LoA)
Level 0: No Automation: At this level, the vehicle is entirely controlled by a human driver, with no automation features present. The driver has full responsibility for all aspects of driving.
Level 1: Driver Assistance: Level 1 automation involves systems that can assist the driver with specific functions. Examples include adaptive cruise control, which automatically adjusts the vehicle’s speed to maintain a safe distance from the vehicle ahead, and lane-keeping assistance, which helps the driver stay within the lane.
Level 2: Partial Automation: Level 2 automation provides more advanced driver assistance systems. At this level, multiple functions can be automated simultaneously, such as adaptive cruise control and lane-keeping assistance. However, the driver must remain engaged and be ready to take control of the vehicle at any time.
Level 3: Conditional Automation: Level 3 automation allows the vehicle to take control of most driving tasks under certain conditions. The driver can relinquish control and engage in other activities, but must be prepared to intervene when prompted by the system. Level 3 vehicles can handle driving tasks in specific driving environments or situations.
Level 4: High Automation: Level 4 automation is considered high automation, where the vehicle can perform all driving tasks within specific driving conditions and environments without requiring human intervention. However, Level 4 vehicles still have limitations, and if they encounter a situation outside their defined operational design domain, the driver may need to take control.
Level 5: Full Automation: Level 5 represents full automation, where the vehicle is capable of performing all driving tasks under all road and environmental conditions that a human driver could handle. Level 5 vehicles have no need for human intervention and can operate autonomously in any situation.
As on date , Level 5 autonomous vehicles are not yet commercially available, and most autonomous vehicles on the roads fall within Levels 2 and 3. The development and deployment of higher levels of automation continue to be an ongoing research and engineering effort.
With advanced vehicle-to-everything (V2X) technologies, connected vehicles equipped with onboard sensors such as cameras or radars and LIDARs have become instinctive for real-time message sharing . However, due to the deficiencies in current technologies and lack of adequate regulations and a framework , there are still immense gaps in the commercial application of autonomous vehicles with high automation. This is still work in progress as a lot of pilots are being conducted in the US and Europe in controlled environments.
5G enablement of Vehicle to Cloud Services
A Connected car depending on the number of miles they drive, there is an estimate that it can transmit anywhere 1.4TB to 19TB per hour and which can translate into almost 380 to 5100 TB per year which is humongous. Given this volume of data the only possible way to store and compute this data would be to leverage the storage, computing power , elasticity and scale of the Cloud. This would require setting up of massive data centres which would need the sufficient compute and storage power to power Cloud services provided by the Hyper scale providers like AWS, Microsoft, Google etc.
Hence the best solution here would be a hybrid architecture where 5G can be used in conjunction with an Edge device to store critical data and respond to events which need near real time response and the rest of the data can be transmitted to the cloud. 5G will be a key enabler with its low latency and high bandwidth to transmit real time vehicle data to the Cloud .
Collaboration and ecosystem development
Collaboration and ecosystem development play crucial roles in the advancement and successful implementation of autonomous driving technologies. Here are some key aspects highlighting their importance:
Technology Integration: Collaboration enables different companies and stakeholders to pool their expertise and resources, fostering the integration of various technologies necessary for autonomous driving. This includes sensor technologies, artificial intelligence, connectivity, mapping, and cybersecurity, among others. By working together, companies can combine their strengths and accelerate technological advancements.
Standardization: Collaboration helps establish common standards and protocols for autonomous driving systems. Standardization ensures interoperability, compatibility, and safety across different manufacturers and service providers. Organizations such as SAE, ISO, and ITU actively collaborate with industry stakeholders to develop standards, guidelines, and regulations that promote safe and efficient autonomous driving.
Data Sharing: Collaboration encourages the sharing of data among industry players, including vehicle manufacturers, technology providers, and infrastructure operators. Shared data can enhance the accuracy of mapping, improve AI algorithms, and facilitate the development of more robust and reliable autonomous driving systems. Data collaboration also assists in addressing challenges like edge cases and rare events that may occur during autonomous driving.
Testing and Validation: Collaborative efforts enable the creation of testbeds, proving grounds, and simulation platforms for extensive testing and validation of autonomous driving technologies. This collaboration ensures that new technologies are thoroughly assessed and validated before they are deployed on public roads, enhancing safety and reliability.
Regulatory Frameworks: Collaboration between industry stakeholders and regulatory bodies is crucial for developing appropriate legal and regulatory frameworks for autonomous driving. Collaboration allows for a better understanding of the technology’s capabilities, risks, and societal impacts, leading to the creation of effective regulations and policies that promote safety, privacy, and ethical considerations.
Ecosystem Development: Collaboration fosters the growth of a robust ecosystem comprising vehicle manufacturers, technology providers, infrastructure operators, telecommunications companies, government entities, and other stakeholders. This ecosystem development helps create synergies, address technical and operational challenges, and promote innovation and market adoption of autonomous driving technologies.
By fostering collaboration and ecosystem development, the autonomous driving industry can leverage collective knowledge, resources, and expertise to overcome challenges, ensure safety, and accelerate the development and deployment of autonomous driving technologies on a global scale.
Trends for the Future
Edge Computing for Low Latency: The deployment of edge computing in 5G networks will bring computation and data storage closer to the network edge, reducing latency. This will enable faster processing of data from connected vehicles, supporting real-time decision-making and critical safety applications.
Augmented and Virtual Reality (AR/VR) Experiences: 5G’s high bandwidth and low latency will support immersive AR/VR experiences in vehicles. Passengers can enjoy interactive entertainment, augmented navigation displays, and virtual experiences during their journeys.
Smart City Integration: 5G technology will foster integration between vehicles, infrastructure, and smart city systems. This integration will enable improved traffic management, optimized parking solutions, efficient energy usage, and enhanced urban planning based on real-time data.