OEMs are shifting from installing black box solutions that offer specialized functions in the more conventional domain architecture to a zone architecture and a function-agnostic processing backbone where each node handles location-specific data. Along with this trend, there is a push toward optimizing sensor functions, fusing multimodal input data with machine learning (ML) for contextual awareness.

Sensors no longer serve one function; instead they can be leveraged in a series of automotive systems from driver monitoring systems (DMSs) to smart door access. As a result, camera and sensor count is minimized and power consumption optimized. A tour of several booths at CES 2025 showed some of the automotive-oriented solutions.

Automotive lighting

Microchip’s intelligent smart embedded LED (ISELED), ISELED light and sensor network (ILaS), and Macroblock lighting solutions can be seen in Figure 1. The ISELED protocol was developed to overcome the issue of requiring an external IC per LED to control the color/brightness of individual LEDs.

Microchip has integrated an intelligent ASIC into each LED where the entire system can be controlled with a simple 16-bit MCU. The solution enables more styling control for aesthetics with additional use cases such as broadcasting the status of a car via text that appears on display-based matrix lighting.

Figure 1: Microchip displayed ISELED lighting solution, where all of these LEDS are individually addressable, allowing designers to change color/brightness levels of each LED. 

Analog Devices Inc.’s 10BASE-T1S ethernet to edge bus (E2B) technology has been used as a body control and automotive lighting connectivity solution. And, while this solution is not directly related to LED control, it can be used to update OEM automotive lighting systems that leverage the 10BASE-T1S automotive bus. 

In-cabin sensing systems

Child presence detection (CPD) and occupancy monitoring system (OMS) were among more pervasive themes, with many companies showing off their ultra-wide band (UWB) detection alongside ranging technology and 60-GHz radar chips. The inspiration here comes from the incessant pressure on OEMs to meet stringent safety regulations.

For instance, The Euro NCAP advanced program will only offer rewards to OEMs for direct sensing systems for CPD. For UWB sensing, the typical setup involved 4 UWB anchors placed outside of the vehicles and two on the inside to detect a phone equipped with UWB. NXP booth’s automotive UWB demo can be seen in Figure 2. As shown in the image, the UWB radar will be able to identify the distance of the phone from the UWB anchor and unlock the car from the outside using the UWB ranging feature with time of flight (ToF) measurements.

Figure 2: NXP’s automotive UWB radar team showcased smart car access solution.

The very same principles can be applied for smart door locks and train stations, allowing passengers with pre-purchased train tickets to pass the turnstile from outside of the station to the inside of it.  

Qorvo also showcased its UWB solution. Figure 3 shows one UWB anchor on a toy car for demonstration purposes. The image also highlights another ADAS application of radar (UWB or 60 GHz): respiration and heartbeat detection. 

Figure 3: Qorvo’s UWB keyless entry and vitals monitoring solutions were exhibited alongside partner companies.

An engineer at NXP granted a basic explanation of the process: the technology measures signal reflections from occupants to detect, for instance, how often the chest is expanding/contracting to measure breathing. This allows for direct-sensing of occupants with algorithms that can discern whether or not a child is present in the vehicle, offering a CPD, OMS, intrusion and proximity alert, and a host of other functions with the established sensor infrastructure.

It’s apparent that there is no clear answer on the number of wireless chips but there is more of a clear requirement that sensors are becoming more intelligent to minimize part-count—a single radar chip could eliminate five in-seat weight sensors. 

Texas Instruments’ CPD, OMS, and driver monitoring system (DMS) can be seen in Figure 4 with a combination of its 60-GHz radar chip and a camera. Naturally, the shorter wavelength 60-GHz radar offers much more range resolution, so this system would likely be more accurate in CPD applications, potentially offering less false positives.

Figure 4: TI demonstrated CPD, OMS, and driver monitoring system (DMS) at CES 2025.

However, possibly the most obvious benefit of utilizing 60 GHz radar is the fact that a single module replaces the 6 UWB modules for CPD, OMS, intrusion detection, and gesture detection. But this doesn’t entirely sidestep UWB technology; the ranging aspect of UWB enables accurate smart door access, and this may be impractical for 60-GHz technology, especially considering the atmospheric absorption at that particular frequency.

AD and surround view systems

Automotive surround view cameras for autonomous driving (AD) and ADAS functions were also presented in a number of booths. Microchip’s can be seen in Figure 5 where its serializers are used in three cameras that can transmit up to 8 Gbps. Microchip’s deserializers are configured to receive the video data and aggregate it via the Automotive SerDes Alliance Motion Link (ASA-ML) standard to the central compute, or high-performance computer (HPC), mimicking a zonal architecture.

Figure 5: Microchip showcased the ASA-ML standard 360 surround view solution at CES 2025.

ADI also exhibited a serializer/deserializer (SerDes) solution with a gigabit multimedia serial link (GMSL) demo. GMSL’s claim to fame is its lightweight nature; the single-strand solution transports up to 12 Gbps over a single bidirectional cable, shaving weight.

Figure 6: ADI’s GMSL demo aggregated feeds from six cameras into a deserializer board, going into a single MIPI port on the Jetson HPC-platform.

Using VLMs for AD

Ambarella, a company that specializes in AI vision processors, conducted an AD demo that integrated large language model (LLM) in the stack. This technology was originally developed by Vislab, an Italian startup that is now an automotive R&D center part of Ambarella.

The system consisted of 6 cameras, 5 radars, and Ambarella’s CV3 automotive domain controller for  L2+ to L4 autonomy. The use of the vision language model (VLM) LLaVA-OneVision enabled more context-aware decision making. 

Vislab founder Alberto Broggi hosted the demo and explained the benefits of leveraging an LLM in this particular use case. “Suppose you have the best perception in the world; so you can perceive everything, you can understand the position of cars, locate pedestrians, and so on,” he said. “You will still have problems because there are situations that are ambiguous.”

He continued by describing a few of these situations. “If you have a car in front of you in your lane, you don’t really know whether or not you can overtake because it depends on the situation,” said Broggi. “If its a broken down vehicle, you can obviously overtake it. If it’s a vehicle that is waiting for a red light, you can’t. So you really need some higher-level description and context.”

Figure 7 and the video below shows one such example of contextual-awareness that a VLM can offer.

Figure 7: Ambarella’s VLM AD demo displayed use case offering  contextual-awareness.  

Aalyia Shaukat, associate editor at EDN, has worked in the design publishing industry for six years. She holds a Bachelor’s degree in electrical engineering from Rochester Institute of Technology, and has published works in major EE journals as well as trade publications.

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