Over the summer, sensor specialist Valeo joined Apollo, the open autonomous driving platform that was created by Baidu (the leading Chinese Internet search company). In an interview with Aftermarket Business World, James Schwyn, CTO of Valeo North America, explained the value of the partnership, as well as the company’s views of the emerging autonomous vehicle market.
Q. Why was the partnership with Apollo undertaken at this point — why this particular partner, and what benefits do you think you will gain?
As part of this partnership, we will contribute our expertise in sensors, an area where Valeo currently ranks as a world leader. Valeo will also contribute value with its innovative sensor cleaning systems, which are key to optimal sensor functioning, connectivity between autonomous vehicles, and control and optimization of air quality in the vehicle’s interior.
In turn, we will benefit from the full range of software, hardware and data tools (operating systems, high-precision positioning and HD mapping services, simulation engines, cloud, algorithms, etc.) provided by Apollo to its ecosystem partners through its open, reliable and secure platform.
Q. Valeo also provides sensor cleaning systems — that’s not a topic that comes up a lot in coverage of self-driving cars, but will obviously be important. How do these solutions work? What are some of the challenges of keeping the various sensors in these vehicles clean?
A. An autonomous vehicle needs clean sensors to be safe to drive. To provide greater visibility in all weather conditions, sensors must be clean, which is why Valeo proposes a full range of systems for washing optical sensors, such as infrared cameras, lidars, radars. These cleaning systems serve to ensure the vehicle’s optical sensors can deliver an uninterrupted video flow that brings greater safety and comfort to drivers.
Recently, we debuted Valeo Everview, the first fully automatic cleaning device for LiDAR sensors. The cleaning system is fitted with a small retractable arm and several nozzles that spray washer fluid to clean the sensors automatically. It’s only 25 ml of cleaning fluid, versus the 100 ml used by the non-automatic systems of our competitors. This allows for a lighter, more compact reservoir, as well as lower vehicle CO2 emissions. The cleaning fluid is sprayed evenly as the arm extends, thereby optimizing the cleaning process across the sensor’s entire surface. As an additional option, Valeo also offers a defrosting feature to ensure maximum performance in winter.
One of the challenges is figuring out how to clean the sensors while limiting the amount of washer fluid required. Limiting the need for washer fluid helps reduce the weight of the vehicle which contributes to the reduction of CO2 emissions.
Q. In which market segments do you think autonomous vehicles will see the earliest adoption — ride-sharing, public transit, commercial vehicles, etc.? What do you see as the biggest barriers to progress when it comes to autonomous vehicles?
A. We’re seeing adoption across each of these market segments. Whether in commercial vehicles, public transportation or rideshare services, cars are increasingly being seen as a “service” for mobility.
I will give you one example. In 2016, Valeo partnered with French startup Navya, which specializes in autonomous shuttles, featuring Valeo sensors. This year at CES, Navya’s Autonom Cab, a fully electric driverless robo-taxi, made its world premiere. The vehicle is fitted with seven Valeo SCALA sensors, which are the first-ever LiDAR scanners to be mass produced for automotive applications. The Autonom Cab delivers a shared mobility service that reduces traffic congestion and pollution while meeting end-user expectations.
In response to your second question, the main challenge is to validate the technology before it goes to market to offer safe and easy to use technologies. Moreover, there are regulations that also need to evolve. It will take time before we see full adoption of autonomous vehicles, but Valeo is making strides in driving this revolution forward.
