Driverless and autonomous vehicles are rapidly transforming the automotive industry, and real-time mobility data streams are powering them. According to a report by McKinsey, autonomous driving will create a forecasted revenue of over $300 billion by 2035. Undeniably, these software-driven vehicles have the potential to revolutionize on-road journeys, ensuring enhanced safety, efficiency, and sustainability.
In this article, we will explore the pros and cons of autonomous or driverless vehicles, highlighting their impact on the automotive industry. But before that, let's deep dive and understand the Five levels of automation.
An Overview of the Five Levels of Automation in Connected Vehicles
With the advent of technology in the automotive industry, OEMs are rapidly incorporating smart technologies in their connected vehicles. Currently, all the premium segment vehicles come with level 1 automation and are forecasted to reach level 3 automation by 2030.
Leading OEMs like Tesla have already introduced connected cars with 2nd level automation features. Here is an overview of these 5 levels of automation:
1st Level of Automation where vehicles come with general hardware connectivity, allowing automated systems to take partial control of the vehicle in specific circumstances. For instance, an adaptive cruise control feature to automatically control the acceleration and braking, specifically in highway driving. So, whenever the drivers are tired, they can take their feet off the pedals while driving.
2nd Level of Automation for partial automation with individual connectivity features that are linked to the driver's digital ecosystem. It allows the vehicle to perform more complex functions by pairing steering (lateral control) with acceleration and braking (longitudinal control).
3rd Level of Automation enables conditional automation in connected vehicles, ensuring preference-based personalization. However, this feature is limited to specific conditions only, such as vehicle speeds, road types, and weather conditions. In case of stop-and-go traffic, the ADAS-equipped vehicles can send live alerts to the driver to regain control during speed anomalies.
4th Level of Automation introduces an intelligent autonomous driving system with multisensory interactions for all occupants. It will help the enterprises monitor the driving environment, send alerts to the drivers, and automate the driving functions if the driver doesn't respond. For example, automatically controlling the speed on the highways with a speed limit if the driver doesn't do it, thus preventing accidents.
5th Level of Automation is a completely driverless or virtual chauffeur environment that is fully automated and comes with intelligent decision-making features.
Now that you know the levels of automation in connected vehicles, let's explore how will they transform the mobility ecosystem.
What are the benefits of Autonomus Vehicles and how do Mobility Data streams empower them?
Undoubtedly, autonomous vehicles are the future of on-road journeys down the years and mobility data streams are going to power them. Here is how:
Safer On-road Journeys
One of the most significant benefits of autonomous vehicles is their potential to minimize human error, a leading cause of road accidents. By leveraging the capabilities of real-time mobility data streams, OEMs can introduce sensor-equipped driverless cars to ensure safer on-road journeys for everyone.
The enterprises or fleet operators can remotely monitor and control their fleets equipped with telematics devices. It's definitely possible by ingesting, streamlining, and transforming the real-time mobility data streaming from the T.C.U. (Telematics Control Units).
Consequently, it will help fleet managers operating these software-driven vehicles to make informed decisions, avoid collisions, and adapt to dynamic traffic conditions, further reducing vehicles' downtime.
Better Operational Efficiency with 24/7 Accessibility
Another perk of autonomous or driverless vehicles is their round-the-clock accessibility, which is impossible with manually operated vehicles. For example, logistics companies can deploy driverless vehicles for deliveries, saving labor expenditures that they would spend on hiring drivers.
Besides that, these autonomous fleets will be continuously operational, allowing enterprises to enhance consumer experiences by adding urgent delivery features to their services. So, consumers can always lap up the benefits of these urgent delivery features if they ever need something urgently.
Emission-free and Sustainable Transportation
With autonomous vehicles powered by mobility data streams, the transport industry can also seamlessly incline towards sustainability. Since most automated vehicles are electric, they don't produce much pollution. Therefore, businesses can switch to green delivery options to ensure environmental sustainability.
But what about completing deliveries within the time commitment when we still don't have an established network of EV charging stations?
Even it is achievable with the help of real-time data streaming from the telematics devices installed in automated fleets. In fact, the future driverless EVs will leverage the capabilities of V2V(Vehicle-to-Vehicle) charging if their battery level goes down.
Cons of Autonomous Vehicles and Mobility Data Streams
Although the market for software-driven vehicles is rapidly transforming the connected mobility ecosystem, there are still some barriers ahead. The major one is to deal with volumes of mobility data streaming from various sources. Let's understand how.
Interoperability Complexities
On top of all the challenges of handling real-time data streams, interoperability complexities for extracting rich datasets from volumes of mobility data. It's possible with fast and seamless data transmission without losing quality mobility data packets.
However, it's only possible by adopting a robust IoT platform that effortlessly ingests data, streamlines it, and transforms it into valuable business insights. Ironically, it's a hypothetical world for most enterprises, especially when it's at a scale.
Unauthorized Third-party Data Access
The integration of mobility data streams raises concerns regarding data privacy and security. Collecting and processing vast amounts of real-time data can potentially compromise individual privacy if not handled responsibly. Ensuring robust data protection measures and adhering to data privacy regulations are essential to building trust in technology.
One way of doing it is by embracing the methods of IoT Network Segmentation. It will allow the processing of the ingested mobility data in various isolated subnets to perform individual tasks.
Go-to-market Challenges
Building applications from scratch and then promoting them is costly and can take months. All thanks to the hectic process the data engineers within an enterprise have to follow to re-engineer codes.
Later on, they need a testing team to validate the application's performance and efficiency. What if the application doesn't perform well? Well, they need to brainstorm complex codes to build production-ready data pipelines. Imagine the time, energy, and cost already consumed to handle the juggle.
Vendor Lock-in Situations Leading to Downtime
Think of a scenario where an enterprise has to forcefully purchase a service that doesn't even align with its business goals. Moreover, they don't even have complete control over their mobility data.
Don't you think it will restrain the enterprise from adapting to the latest technologies of the market which can bring tangible ROI (Return on Investment)? A vendor lock-in situation feels exactly the same.
Even if enterprises subscribe to the IoT solutions from third-party vendors, it's costly on a scale, further affecting their overall budget.
A Robust Data Streaming Architecture: Need of the Hour
To overcome the aforementioned challenges, the industry needs a robust data streaming architecture to derive valuable insights from the volumes of mobility data coming from heterogeneous sources. Here is where they need a verticalized mobility IoT platform to streamline the process. It's only possible by incorporating a robust data streaming architecture in their existing IoT infrastructure. Besides that, it should be:
Scalable enough to seamlessly integrate new IoT devices as the demand increases, optimizing infrastructure costs
Low Code/No Code (LCNC) Platform with a user-friendly interface, further reducing go-to-market time
Easily available on various cloud marketplaces like AWS, GCP, Microsoft Azure, etc., minimizing vendor lock-in situations
Final Thoughts, Autonomous vehicles powered by mobility data streams are already the talk of the entire automotive industry and that too for good reasons. And after reading this article, you've already understood why. A robust mobility data architecture is the backbone of this revolutionary change in automotive technologies.
Entreprises are already switching to verticalized mobility IoT platforms to rapidly upgrade the levels of automation in their connected vehicles with utmost efficiency. Without a doubt, it's going to multiply and pave the way for a smarter, safer, and sustainable future of transportation.