Wireless 5G communications needs to chalk up a flawless safety track record before consumers will buy into self-driving vehicles.

Perhaps the single largest hurdle to a future in which autonomous vehicles (AVs) rule the road is consumer resistance.

Numerous studies provide evidence that AVs have a much better traffic safety record than human beings—their more-experienced and more-traditional counterparts. Nevertheless, consumers are reluctant to have the steering wheel ripped out of their hands, being reduced to being passengers dependent on machines.

As AV technology improves and becomes more familiar, this attitude is likely to shift over time: consumers will likely become less squeamish about it. Until then, consumer safety concerns will continue to be a significant perspective to overcome.

Overcoming the squeamishness

Consumer concerns about the safety of AVs put all aspects of their development under a magnifying class—from the developing chipsets and components all the way to rolling finished vehicles off the production lines. This heightened consumer awareness of AV safety underscores the importance of proper test methodologies at all stages of the supply chain.

The Society of Automotive Engineers defines six levels of vehicle automation, from Level 0 (no driving automation) to Level 5 (full automation). Level 5 vehicles will be fully autonomous, lacking any human input other than a destination. These cars will not have steering wheels or brake pedals.

What Level 5 vehicles require is 5G technology for ultra-fast, reliable communications with other vehicles, roadside infrastructure, pedestrians and cyclists, and the cloud for traffic and weather conditions. AVs require advanced technologies and instruments to help the car see the road and react to changing conditions. These technologies include radar, lidar, artificial intelligence and advanced sensor systems. Data gathered by these systems helps individual vehicles to safely navigate the road based on 360-degree field of vision.

AVs will share such sensor information with nearby cars to enable advanced autonomous driving maneuvers such as vehicle platooning. This level of communication depends on speed and reliability. For example, in a scenario that requires the sudden application of the brakes by all vehicles traveling on a road, autonomous driving systems may need to send out an immediate warning to vehicles behind, to prevent a chain collision. Such a warning will be effective only if it reaches the cars in time to take immediate evasive action.

The speed and reliability that AVs require is achievable with 5G communications. One of the most widely-lauded use cases for 5G is ultra-reliable low-latency communications (URLLC), which mandates latency of no more than 1 millisecond in some cases, and reliability of 99.999%. For AVs, which absolutely depend on the ability to communicate with the cars around them, this technology is a necessity, and it can help allay consumer doubts and fears as it becomes ubiquitous and proven as a trustable technology.

Challenges in mmWave chipset design

For engineers designing chipsets for AVs, the incorporation of 5G brings many new challenges to the table. Among these challenges are flexible numerologies, millimeter-wave (mmWave) design considerations, massive multiple-input / multiple-output (MIMO), and beamforming challenges.

The combination of these technologies, the 5G requirement for over-the-air (OTA) testing, excessive path-loss issues for mmWave frequencies, and the specific challenges associated with chipsets for automotive (such as the use of vehicle-mounted antennas) compound the challenges of AV chipsets.

In terms of signal quality, many factors such as baseband signal processing, modulation, filtering, and up-conversion need to be addressed. At the higher frequencies and wider channel bandwidths of mmWave, AVs face signal impairments that become more problematic.

Interference issues are also a concern. Certain properties inherent in orthogonal frequency-division multiplexing (OFDM) systems prevent interference between overlapping subcarriers. However, issues such as in-phase/quadrature (I/Q) impairments, phase noise, linear (AM to AM) and nonlinear (AM to PM) compression as well as frequency error, can cause distortion in the modulated signal.

In mmWave OFDM systems phase noise is one of the most challenging factors. Too much phase noise in a system results in an error vector magnitude that is too high, leading to impaired demodulation performance. Too much phase noise also causes subcarrier interference, further impairing demodulation performance.

Other design issues that require finetuning include new challenges in path loss, blockage, and signal propagation. Operating at mmWave frequencies increases bandwidth but introduces these problems because of the shorter wavelength of mmWave transmissions, as well as physical obstacles in the channel (including other vehicles) that can block the signal. The use of vehicle-mounted antennas exacerbates these issues.

Beamforming, a method of providing discrete control of the direction of a transmitted beam, is a key technology used to overcome the propagation issues that afflict mmWave transmissions. As a result, mmWave transmissions are highly directional, requiring higher-gain active antennas with electrically steerable direction. However, since the body of a vehicle acts as a large ground plane located right next to the antenna, mmWave transmissions create a host of additional antenna testing challenges and budget-management complexities.

In view of the challenges described above, the process of AV chip designs requires test solutions to measure and characterize signal quality accurately without introducing new issues. Only then can autonomous vehicle safety hit exacting levels of reliability that inspire growing consumer confidence.