Beyond Vision: Why Radar is the Gamechanger for Autonomous Driving in India
NXP
Semiconductors.
India is at a crucial point in its journey toward
autonomous mobility
with a vision of obtaining full autonomy, which is still on the horizon, but the groundwork is being laid with
ADAS
becoming more prevalent in mid-range and premium vehicles. A recent report by McKinsey & Company estimates that by 2030, up to 20–30% of new cars sold in India could come with at least
Level 2 autonomy
, including features like adaptive cruise control and lane-keeping assistance.
The government's push through policies like the PLI scheme for automotive and auto components is further creating a fertile ground for this transformation. As the sector evolves, vision-based systems, while powerful face critical limitations in real-world conditions. Enter radar: a technology that is proving indispensable in ensuring safety, precision, and reliability, especially in India's complicated driving environments.
Radar in the Real World: Where Vision Falls Short
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Undo
Unlike cameras and LiDAR, which rely heavily on visual data, radar operates on the principle of electromagnetic wave reflection. This allows it to "see" through dense fog, heavy rainfall, pitch darkness, and even dust, scenarios that are all too common across India's varied geographies.
Since India's traffic isn't just chaotic; it's also unpredictable, the traditional vision systems can get overwhelmed by the diversity of movement patterns and low-contrast situations. Therefore, Radar systems, with their capability to detect objects up to several hundred meters away, and their resilience in low-visibility conditions, become important allies in enhancing road safety.
The Technology Within: Making Radar Affordable and Scalable
Further to this development, a new generation of radar microcontrollers are designed specifically for ADAS applications. One such chip is the S32R47, a 32-bit radar processor built on 16 nm FinFET (Fin Field-Effect Transistors) technology. It integrates high-performance signal processing capabilities with multi-core compute, enabling denser point cloud output and enhanced algorithms that enable next-generation driving automation systems from Level 2+ up to Level 4.
What sets this chip apart is its ability to deliver imaging radar performance at scale, , alongside improved system cost and power efficiency. The cost efficiency makes it possible to accelerate the roll-out of
4D imaging Radar
across all regionsThe S32R47 supports 4D imaging radar, enabling vehicles to accurately detect, classify and track objects, whether they are small or large, still or moving, that are relevant for the vehicle to make driving decisions, It also creates a detailed, real-time map of the vehicle's surroundings.
For standard front and corner radar applications, NXP was first to introduce a radar one-chip SoC (System-on-Chip) built on 28nm RFCMOS technology, the SAF85xx, enabling implementation of NCAP (New Car Assessment Programme) features, including automated emergency braking and blind-spot detection. It is comprised of a highly integrated RF front-end and a multi-core radar processor.
By combining standard or imaging radar with vision systems, manufacturers can create sensor fusion architectures that are scalable, cost-effective and reliable. This makes autonomy more accessible, for both mass-market vehicles and high-end models.
Challenges and the Road Ahead
Implementing radar-based systems comes with its own set of hurdles. One major challenge is spectrum allocation, as automotive radar operates in the 76–81 GHz band and requires interference-free frequency access to function optimally. Policy-level clarity and dedicated spectrum for automotive radar will be critical to ensure consistent performance in urban and highway scenarios.
Another barrier is the lack of indigenous manufacturing for key radar components such as antennas, RF front ends, and MMICs. To overcome this, India needs a focused push toward localizing component production through technology transfers, public-private partnerships, and startup incubators focused on automotive semiconductors.
Software also presents a complex challenge. Radar systems must be trained on Indian traffic patterns and road behavior, which can differ significantly from global datasets. Tackling this requires creating large-scale, localized radar datasets and simulation environments to fine-tune performance under diverse Indian driving conditions—from congested city roads to high-speed expressways.
Encouragingly, progress is already underway. Global OEMs are investing in India-based R&D hubs, local suppliers are ramping up radar module production, and the use of advanced simulation and testing platforms is accelerating product readiness for Indian conditions.
Conclusion
The road to autonomous driving in India will not be paved with just vision, it will require the invisible clarity that radar provides. As we move toward a future where safety, accessibility, and scalability are paramount,
radar technology
emerges not just as a complementary component but as a foundational pillar of that vision.
To unlock the full potential of autonomy in India, we must go beyond vision, and radar is showing us the way.
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