Why Software Is Driving Modern Vehicle Recalls
· Free Press Journal
According to industry experts, software-related integration failures are becoming an increasingly visible factor in modern vehicle recalls.
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As vehicles become increasingly software-defined, the root cause of many modern safety recalls is shifting away from purely mechanical defects and toward software behavior that was never fully exercised before release - especially when multiple systems interact under real-world uncertainty.
Vijay Saravana Jaishanker, a software engineer focused on safety-critical vehicle validation, software-defined vehicle systems, and ML-enabled autonomy and an inventor on multiple vehicle software testing patents, says the industry’s challenge has shifted from verifying individual components to validating system-level behavior.
“Today’s vehicles are complex distributed systems,” Jaishanker explains. “Multiple embedded controllers, perception and control software, over-the-air updates, and cloud-connected services all interact with each other. Validating each piece in isolation is no longer sufficient.”
In many software-driven issues, the defect is not obvious when a single component is tested on its own. Problems emerge when modules interact under real-world uncertainty, such as timing variation, sensor noise, network delays, or unexpected combinations of events. These interactions can produce system behavior that was never anticipated during development. The challenge is intensified as machine learning becomes more common in autonomy-related functions, where perception outputs can vary significantly with lighting, weather, occlusion, and edge cases that are difficult to enumerate exhaustively.
Scale further compounds the problem. The number of meaningful scenarios grows far faster than any purely physical testing program can cover. Road testing remains essential, but it is not designed to systematically explore rare yet consequential combinations of conditions, nor can it keep pace with frequent software releases and over-the-air updates.
“You cannot rely solely on physical road testing to discover every meaningful failure mode,” Jaishanker says. “The scenario space is simply too large.”
To address this gap, he advocates for simulation-driven validation pipelines that allow engineering teams to exercise vehicle software under controlled variation before it ever reaches customer vehicles. In practice, this involves combining multiple approaches, including virtualized vehicle environments, model-based testing, and hardware-in-the-loop setups. Together, these methods make it possible to reproduce failures reliably, explore system-level interactions, and catch regressions earlier in development.
Late discovery remains a recurring driver of recalls. When safety-relevant defects are identified only after vehicles are already in customer hands, fixes become costly, operationally complex, and reputation-damaging, even when a software update is technically feasible. Shifting validation earlier into design and integration reduces both safety risk and downstream recall impact by catching issues when they are cheaper to diagnose and safer to correct.
“The later you find a defect, the more expensive it becomes and the higher the real-world risk,” Jaishanker notes. “If validation happens earlier, both safety exposure and recall fallout can be significantly reduced.”
What This Means for Engineering Teams
For teams building modern vehicle software, the implication is clear. Many safety issues no longer originate in individual components, but in how software systems interact under real-world uncertainty. Preventing avoidable recalls therefore requires validating system-level behavior much earlier in development, using simulation to explore rare but high-risk scenarios at a scale that physical testing alone cannot achieve.
This perspective is reflected in Jaishanker’s technical work, including his contributions to patented approaches for vehicle software testing and simulation of a virtual twin. The common objective is to help engineering teams reason more rigorously about vehicle behavior and identify safety-relevant issues before deployment.
He emphasizes that smarter validation is not about replacing engineers with automation. Instead, it is about giving engineers leverage, enabling them to prioritize the highest-risk behaviors, reproduce subtle failures, and validate complex system interactions at a scale that matches modern vehicle complexity.
“The real value comes from combining engineering judgment with data-driven validation,” Jaishanker says. “When that balance is achieved, recalls become the exception rather than the norm.”
As vehicles continue to evolve toward higher levels of autonomy and software control, he believes the strength of a company’s validation capability will increasingly define its safety leadership. The differentiator will not be how quickly defects are fixed after deployment, but how effectively they are prevented from reaching customers in the first place.