Industry Voices: Next-Generation Mobility Needs Game-Changing Sensors

Opinion piece by Max Liberman, Uhnder’s vice-president of chips for AV radar.

One of the benefits of living in today’s world of ultra-fast-moving technological advancements is that we get to see our dreams become reality in a matter of a few years.

We have watched personal vehicles become smarter, safer, and more efficient by the day. In fact, some technologies found in our cars today are so common that many of us already take them for granted. However, for the next generation of mobility to have a revolutionary impact, developers must discern which technologies are suited to long-term growth and sustainability, and which are mere red herrings.  This is particularly difficult when this space is overcrowded with ambitious players, many of whom are doubling down on hype or legacy technologies.

This is our fear when it comes to autonomous mobility. Traditional analog radar and LiDAR systems may be trusted and familiar sensing solutions for autonomous developers, but they are limited in ways that digital radar solutions are not. Such limitations might be workable in the short-term, but in the eventual shift toward entirely autonomous vehicles, they could potentially have life or death implications.

Perception Sensors of the Future

One specific problem autonomous mobility designers face is determining what combination of perception sensors to use: LiDAR, cameras, traditional ‘analog’ radar, digital radar. Perception sensors in autonomous vehicles are literally replacements for human eyesight, so collectively they need to be 100 percent accurate, 100 percent of the time. Otherwise, accidents can, and do, occur.

Each sensor has its own strengths and weaknesses. Both radar and LiDAR can determine how far away an object is. Cameras struggle to do this. Both LiDAR and cameras can tell you what the object is, whereas traditional radar struggles with object classification. Both cameras and LiDAR struggle to “see” in harsh weather conditions including fog, whereas radar is not affected by it. While the wavelength of LiDAR currently affords it superior angular resolution, novel developments in digital radar, including its ability to have many virtual channels on a single integrated circuit (IC), is closing the object classification gap between LiDAR and traditional ‘analog’ radar.

Building off these traditional benefits of radar, digital radar adds higher angular resolution with fewer components and the ability to minimize mutual interference using digital code modulation (DCM). Digital radar’s ability to eliminate mutual interference is significant as a 2018 National Highway Traffic Safety Administration (NHTSA) report found that traditional radar “may suffer significant degradation of performance in radar-congested environments”.

Other considerations for LiDAR include its relatively high production cost, difficult installation processes and reliability issues. For example, LiDAR systems can be easily blinded by splattered insects. Radar is easier to install, costs less and is a proven automotive technology.


In all fairness, emphasis on LiDAR in development circles is an understandable error. After all, LiDAR initially seemed like the technology of the future, especially for autonomous mobility, and radar, before its digital modifications, seemed like a relic of the past. LiDAR’s impressive resolution alone was enough to praise it out of the gate but developers are quickly learning of its practical limitations, which continues to delay large-scale LiDAR adoption. Alternatively, digital radar solutions are breaking new ground in both safety and functionality. Developers can easily change course, and they would be wise to look at how digital radar might solve the challenges that LiDAR can’t seem to overcome, such as visibility, cost, and commercialization.

Digital radar holds significant promise for a fully autonomous transportation future, which has roused our imaginations for decades.  By inspiring autonomous developers, we may soon realize our collective dream.

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