In the fast-evolving automotive industry of today, technological innovation is no longer just a competitive advantage but a necessity for survival. As the industry transitions to a more connected, software-driven future, automakers face increased stakes. By 2030, an estimated 95% of new vehicles will be connected, opening significant opportunities. The Boston Consulting Group predicts that software-defined vehicles could add over $650 billion by 2030, making up to 20% of the market's total value. Automakers who invest strategically in these technologies stand to greatly enhance their revenue and brand leadership.

As software takes center stage, traditional validation and testing approaches that are reliant on late-stage testing are proving inadequate and, hence, must evolve to keep pace with the complexity, scale and continuous evolution of SDVs. The traditional hardware-centric sequential validation model, characterized by late-stage software testing, is too slow and rigid, often requiring extensive physical testing. This delays the rollout of new features, posing integration challenges for updates and the ability to respond to real-world data and technological advancements. Additionally, the cycle is resource-intensive, making it difficult to optimize or scale autonomous systems rapidly. Moreover, traditional simulation faces obstacles in accurately mirroring complex real-world conditions and computational limitations can also cause discrepancies between simulated and actual performance, impacting reliability and safety. To match the pace of rapid software development and shifts in electrical/electronic (E/E) architecture, the industry must rethink its validation strategies to boost efficiency, foster innovation, improve safety and lower development costs.

Shift-Left approach: Validating early, testing continuously

The ‘Shift Left’ approach is a strategy that integrates testing, QA and risk mitigation earlier in the development lifecycle — during planning, design and coding. By leveraging automation, continuous integration and DevOps, defects are identified sooner, reducing time-to-market and minimizing late-stage fixes. This fosters a more agile, resilient process by embedding quality checks into each iteration.

Virtual validation

Virtual validation simulates real-world driving conditions and system interactions using virtual models (hardware and software), enabling early validation of complex systems like Advanced Driver Assistance Systems (ADAS) and autonomous driving features in a controlled environment before physical testing. This approach helps automakers spot potential issues and optimize performance, boosting reliability and cutting costly changes later in the cycle.

Why virtual validation is essential for the automotive industry

• Cost efficiency: Virtual validation reduces the need for expensive physical prototypes and testing, cutting R&D and manufacturing costs, which run into millions for OEMs.
• Speed to market: In an industry where time-to-market is becoming increasingly critical virtual validation expedites the product development process, enabling quicker design and simulation iterations and reducing development and testing time.
• Enhanced safety and quality: Virtual validation allows for more thorough and controlled testing. OEMs can simulate a wide variety of scenarios, from crash tests to handling dynamics, ensuring the safety of vehicles and complying with the highest quality standards before they are built.
• Regulatory compliance: With stricter emissions, safety and sustainability regulations, virtual validation provides a robust platform for testing and empowering OEMs to ensure compliance before physical models are built.
• Fostering innovation: Virtual validation enables experimentation with new designs and materials that might be too risky or costly to test in the real world, unlocking the door to bold innovation, particularly in areas like Electric Vehicles (EVs) and autonomous driving.

Why OEMs are struggling to fully adopt virtual validation

Despite significant investments in AI modeling, cloud-based platforms and continuous development and integration strategies, the automotive industry faces hurdles in integrating modern software with legacy systems to ensure cybersecurity and adherence to evolving regulatory standards.

Based on my experience at HCLTech, the key challenges included:

• Data complexity and integration: Virtual validation relies on extensive, precise data to replicate real-world conditions. Integrating this data across departments can create silos and inefficiencies and challenges in harmonizing digital tools with existing systems.
• Resistance to change: The automotive industry, rooted in tradition, relies on physical prototypes and real-world testing. Shifting to a virtual validation model necessitates a cultural change at all levels, with some engineers skeptical of relying solely on simulations.
• Talent shortage: Virtual validation demands professionals skilled in advanced simulation, machine learning, data analytics and cloud engineering, but there is a noticeable shortage of such talent.
• Legacy systems: Many OEMs continue to work with legacy technologies that might not be optimized for integration with modern simulation tools. Transitioning from traditional systems to cutting-edge digital platforms can be complex, time-consuming and costly.
• High initial investment: Significant initial investments in implementing virtual validation technologies and acquiring tools, hardware and data storage can be cost-prohibitive, especially for smaller or legacy manufacturers.
• Regulatory concerns: Despite virtual validation's accuracy, certain regulators still mandate physical testing for certification, making OEMs hesitant to fully embrace it, particularly for essential safety tests like crash simulations.

Navigating the path to widespread adoption

At HCLTech, we've worked with numerous OEMs to help them navigate the challenges of implementing virtual validation. To overcome these barriers, OEMs must use the following strategies:

• Strategic partnerships: Collaborate with tech providers, software vendors and digital transformation consultants to bridge technical and expertise gaps. By leveraging the strengths of these partners, OEMs can accelerate their adoption of virtual validation tools.
• Phased integration: Adopt phased approach to adopt virtual validation rather than a full-scale shift overnight to manage the transition more effectively.
• Investing in talent development: The automotive industry must place greater emphasis on nurturing talent skilled in software testing, simulation, AI and data science. Training programs, academic partnerships and collaborations with engineering service providers are crucial.
• Optimizing legacy systems: Modernize existing IT infrastructure to ensure new digital tools integrate smoothly with legacy systems, facilitating a seamless transition to virtual validation.
• Engaging with regulators: Work proactively with regulatory bodies to show how virtual validation can augment physical testing and adapt as standards evolve.

The foundation is set with vECU Level 4, incorporating simulated hardware validation and regression tests before physical hardware usage. HCLTech's Test Sphere ecosystem is pivotal for crafting virtual test benches and the company is advancing towards Level 3 autonomy and ADAS testing, focusing on hands-free driving capabilities.

HCLTech is constantly focusing and investing in a 'shift left' approach in its core work with compute and embedded systems, focusing on integrating key processes such as design, development and testing earlier in the product lifecycle. Our team of experts has made significant strides by conceptualizing the design and development of the SIL test bench. Alongside this, we continuously leverage automation and cloud migration, as well as extensively use of our in-house KDT Framework to drive efficiency and innovation throughout the development process.

Future outlook: From simulation to self-learning systems

The coming decade will merge virtual validation, AI and digital twins, revolutionizing vehicle testing and development. AI-enhanced simulations and digital twins will enable more complex and efficient testing scenarios, dramatically reducing the time and cost associated with physical trials.

• AI-driven simulation: AI enhances modeling and analytics, accelerating validation with complex driving scenarios and real-time optimizations.
• Digital Twins: Real-time digital replicas of vehicles or systems provide continuous monitoring, testing and optimization throughout the development lifecycle. They also provide valuable insights into vehicle behavior, performance and potential failure points, facilitating more accurate and proactive validation of automotive systems, especially for advanced technologies like ADAS and autonomous driving.
• Cloud-based validation: Cloud-based platforms offer scalable computational resources that facilitate vast data handling from simulations and testing, enabling real-time global collaboration and continuous integration and testing, hence providing quicker feedback loops. Further, they support faster iteration cycles, improved scalability and the ability to simulate a wide variety of conditions that would be impractical or costly to replicate physically.

The future of automotive development

The automotive industry’s future is deeply intertwined with digital transformation, centered around virtual validation. While challenges persist, the advantages — streamlining development, reducing costs, enhancing safety and boosting innovation — are significant. For OEMs mastering these complexities, substantial rewards await.

At HCLTech, we are dedicated to helping automotive manufacturers maximize the benefits of virtual validation. By leveraging simulation and data-driven insights, OEMs can achieve more efficient, intelligent and sustainable vehicle development, ultimately driving the next era of automotive innovation.