ADAS technology is emerging as an essential feature for the automotive industry and has been a key player in reducing road accidents so far. According to an analysis by Mckinsey, ADAS and autonomous-driving technology can create approximately $400 billion in revenue by 2025.

But what if the ADAS system fails? Will things still remain the same? Here is where OEMs require an effective solution for managing the ADAS applications installed in their vehicles. One of them is the management of vehicle data to execute the OTA updates of the ADAS system in real time. Before discussing how it happens, let’s have a quick recap of ADAS technology and the challenges associated with its implementation.

What is ADAS Technology and How Does it Work?

ADAS or Advanced Driving Assistance System is a suite of safety features specifically designed to enhance safe on-road journey experiences in connected vehicles. This system is operational due to the strategic integration of specific sensors throughout the interiors of the vehicle.

These sensors have a wide range of safety features with different communication protocols to collect and transmit relevant data packets to various systems and applications. Furthermore, it allows the vehicle operator to analyze the data and alert the driver to take immediate action if an event occurs.

ADAS Features in the Automobile Industry and Connected Vehicles

Enlisted below are some of the components and use cases of the ADAS feature in connected vehicles:

• Lane Change Assistance (LCA) for monitoring adjacent lanes and alerting and assisting drivers to safely change the lane

• Blind Spot Detection (BSD) for alerting and warning drivers about vehicle overtakes by detecting approaching vehicle/object

• Forward Collision Warning (FCW) for detecting potential collision situations in tight spaces, such as parking areas

Although the ADAS functionality is an essential feature that unicorn OEMs and enterprises are implementing into their vehicles, there are still a lot of challenges on their way. Let’s explore each of them in detail.

ADAS Implementation: An Ongoing Juggle for OEMs

Handling ADAS data coming from one geographical location is easy. But what about the volumes of data coming from disparate sources available in different geographies? It’s the moment where managing this data for actionable insights is the most challenging task. Enlisted below are a few of the hurdles.

1. Scalability Challenges Due to Route Changes

OEMs still find it a tough journey to come up with effective solutions that are not restricted to predefined routes. Moreover, the Lidar sensors are constantly advancing each day. Furthermore, it creates room for complexities while implementing map modules and rule-based coding in connected vehicles operating in different locations.

2. Technical Incapabilities During the Testing Stage

OEMs require a high volume of data to support extensive testing and validation during the development process of the ADAS system. It means another room for more effort and technical skills to manage and analyze mass data coming from different environments and scenarios.

Will it be a cakewalk with the advancing technology in connected mobility when OEMs have to build solutions based on the ingested and transferred data? The answer is a “No.” One of the reasons is the absence of a detailed virtual environment for reflecting the pass/failure criteria for different use cases.

3. High Implementation & Maintenance Cost

Another barrier across the implementation of ADAS technology is the expensive repair due to uncertain component failures. Mostly, it happens due to the complexities of handling this system, further leading to:

• Relatively higher implementation cost to combat the compatibility differences in hardware, software, and communication protocols in the vehicle’s existing system

• Inaccurate readings and system malfunctioning due to OTA update failures

• Delayed updates of ADAS-related firmware and associated firmware integrated into a large fleet of vehicles

• Additional expenses for maintaining human resource requirements resulting in intangible ROI

  
  

4. Compliance and cybersecurity challenges

Since ADAS exposes a large attack surface, ensuring a safe environment for it is a concern for OEMs. However, it’s sometimes like swimming against the tides when it comes to overcoming cybersecurity challenges in advanced driving assistance technology.

Although OEMs and suppliers have created internal organizations to deal with cybersecurity challenges, organizational silos are a hurdle for them. Besides that, they often encounter scalability issues with real-time data while integrating Vehicle-to-Everything(V2X) communication and back-end connectivity.

5. Unconsented Data Sharing with External Partners

Almost all premium OEMs and enterprises operate multiple vehicle models with different hardware and software integrations. It’s where they rely on third-party providers for API integrations.

But do they have complete control over the interfaces and data sets they actually want to share with the external partners? What if the third-party providers who get access to the data OEMs didn’t even give consent? These are a few issues that these companies still struggle to address.

6. Unavoidable Bills for Unused Data

Although OEMs and enterprises understand the importance of data management, they often don’t know how much of it is useful for them. Even according to Seagate’s ‘Rethink Data’ report of 2020, 68% of the data available to enterprises go unleveraged.

Bearing this additional data storage cost is painful. Implementing a Pay-as-you-go model for auditing and billing the OEMs as per the utilized storage can be a saviour.

Undeniably, the list of challenges with the implementation of the Advanced Driving Assistance System is huge. The only way to get through this barrier is by switching to an effective IoT platform for ingesting and handling volumes of real-time data pipelines with ease. It’s possible with Condense and Condense Edge where the former powers the latter.

Condense: A Streamlined Platform-based Application to Overcome ADAS Challenges

Condense is a low-code/no-code, click-to-deploy managed application that lets OEMs and enterprises effortlessly ingest and manage huge volumes of real-time data by:

• Incorporating an effective data management infrastructure and software to automatically handle incoming petabytes of semi-structured and non-structured camera and sensor data.

• Reducing data latency by digital twinning the vehicle, further improving the accuracy of the autonomous system

• Adopting a BYOS (Bring Your Own Subscription) model, allowing OEMs to have their vehicle’s data on their cloud and further helping them to extend their existing data pipelines

• Downsizing the attack surface in connected vehicles by including various proprietary data protocols and authentication mechanisms

• Switching to one platform for all the ADAS-related software updates, loggings, commands, and diagnostics, reducing multi-vendor integration dependency, complexity, and cost

Wrapping it up,

The ever-growing ADAS technology in connected mobility has already saved lives, is still developing, and will continue to evolve in the future too. However, OEMs and enterprises are still finding the best technologies to efficiently manage volumes of real-time data for handling ADAS-equipped applications.

These technologies will further help them in reducing scalability challenges, technical incapabilities during the testing stage, dependency on third-party proprietors to build their solutions, and much more. Zeliot’s Condense and Condense Edge are making it possible.