In the 1950s the philosophy informing the futuristic vision of autonomous vehicles was to “make the road guide the car” via electronics and mechanical devices embedded in high tech roadways. Scientists did not seriously consider building smart tech or advanced sensors into the vehicles themselves: it would have been impossible to stuff the 30-ton computers of the time into a Ford sedan; radar and lidar were only seen on advanced fighter aircraft; and the concept of computer vision software had yet to be conceived.
A generation later, attempts at Vehicle-to-Everything (V2X) systems and the US Smart Highway Project of the 1970s and 1980s also failed to gain traction, simply because the required tech had not yet been sufficiently developed.
The machine learning and tech explosion of the last few years has opened up the previously impossible, and self-driving vehicles have arrived. Last month, the Alibaba DAMO Academy and China’s Ministry of Transport Research Institute of Highways (RIOH) launched a new joint lab to tackle the challenge of smartening up the country’s roads.
At the centre of the project is Alibaba’s IntelliSense base station, a hardware device designed to be installed in autonomous vehicles’ operating environment, usually atop streetlights or roadside billboards. Consisting of a variety of sensors and computing units, the base station helps guide self-driving vehicles while also monitoring other vehicles and outside entities. It’s as if there were an attentive traffic cop every two hundred meters along the route.
Synced recently spoke with Gang Wang, who joined Alibaba AI Labs in March and is responsible for the company’s R&D in machine learning, computer vision, and natural language understanding. Wang is a former associate professor at Nanyang Technological University in Singapore and Associate Editor of the IEEE journal Transactions on Pattern Analysis and Machine Intelligence.
Wang says that during the Alibaba team’s testing on multiple open road sections, if the IntelliSense base station was deactivated the self-driving vehicle was unable identify an obstacle such as a pedestrian rushing into the lane at distances less than two meters from the vehicle. Activating the base station enabled the vehicle to sense such obstacles in advance and avoid them successfully.
What happening on roads reflects what’s happening in tech company boardrooms and in Chinese government initiatives. And in this regard there are two main concerns.
How effectively can the government’s infrastructure transformation projects be synchronized with driverless technology development, and what role should tech companies play? According to the Ministry of Transport, China has 4,773,500 kilometers (almost 3,000,000 miles) of national highways. The time and capital cost of digging them all up to install sensors would be immeasurable. Could a “post-installation hardware” such as Alibaba’s IntelliSense be a solution? There has been no official word yet on the hardware’s cost or any government procurement.
In a sense, what Alibaba is doing is similar to the strategy of Didi, the owner of Uber China. In the past year Didi has been actively seeking technical cooperation with the government in assembling intelligent signal lights in various cities, developing reversible lanes, and using self-generated vehicle network data to improve driving efficiency. Didi’s AI leverages real time data from its rides to change traffic lights automatically. Reversible lanes meanwhile is a system that changes the direction of underused lanes on a roadway to alleviate congestion. Again, data on traffic volume and vehicles’ speed helps Didi determine when lanes can be reversed and when. Authorities in Jinan, Shenzhen, and Wuhan have been working with Didi on reducing congestion using reversible lanes.
The second concern is whether the improvements that smart roads offer to autonomous driving performance and safety are worth the expensive and time-consuming infrastructure upgrade. As even a state-of-the-art L5 (Complete Automation) vehicle cannot achieve 100% safety, would adding smart roads allow it to do so? Or, could smart roads elevate a “High Automation” L4 vehicle to L5 capability?
Moreover, building smart roads does not ensure that the different autonomous vehicles using them will be safe. The system will be rendered useless for example if data transmission is interrupted, if transmission standards vary between vehicles and environments, or if a car manufacturer or owner restricts data access. Wang told Synced that “solving this problem still requires continuous participation and discussion from all parties who might be involved.” Even if the benefits of an AI system are clear, bringing different players on board is a challenge in any industry introducing the tech.
Although the idea of designing or transforming smart roads has been discussed for decades, at this stage it’s still difficult to see strong short-term profit potential for such an undertaking — and cost remains the main stumbling block.
If anyone has the money, know-how and patience to develop and deploy a smart roads strategy it is global tech powerhouse Alibaba, which is valued at over US$500 billion.
Alibaba’s DAMO Academy (Academy for Discovery, Adventure, Momentum and Outlook) was established to increase global technological collaboration, advance the development of cutting-edge technologies, and make the world more inclusive by narrowing the technology gap.