Mapping rural areas is a barrier to autonomous vehicles operating in those areas, but cars might not need maps at all.
Image: MIT CSAIL
Google has spent the last 13 years mapping every corner and crevice of the world. Car makers haven’t got nearly as long a lead time to perfect the maps that will keep driverless cars from sliding into ditches or hitting misplaced medians if they want to meet their optimistic deadlines.
This is especially true in rural areas where mapping efforts tend to come last due to smaller demand versus cities. It’s also a more complicated task, due to a lack of infrastructure (i.e. curbs, barriers, and signage) that computers would normally use as reference points. That’s why a student at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) is developing new technology, called MapLite, that eliminates the need for maps in self-driving car technology altogether. This could more easily enable a fleet-sharing model that connects carless rural residents and would facilitate intercity trips that run through rural areas.
In a paper posted online on May 7 by CSAIL and project partner Toyota, 30-year-old PhD candidate Teddy Ort—along with co-authors Liam Paull and Daniela Rus—detail how using LIDAR (a radar-like sensor that uses lasers instead of radio waves to measure distances) and GPS together can enable self-driving cars to navigate on rural roads without having a detailed map to guide them. The team was able to drive down a number of unpaved roads in rural Massachusetts and reliably scan the road for curves and obstacles up to 100 feet ahead, according to the paper.
“Our method makes no assumptions about road markings and only minimal assumptions about road geometry,” wrote the authors in their paper.
Most of the autonomous navigation tools we’ve seen so far—from TomTom, Waymo, Google, and those developed by automakers—involve driving down a road with a manned vehicle first to capture high-definition maps of the road, then later using those maps to train a driverless car’s onboard computer to perform the same task. These HD maps rely on lane markers, curbs, medians, and other road characteristics to inform navigation. Ort had initially set out to do similar work.
“But then I started to see how much effort it takes to build these maps and how much effort it takes to maintain them,” he told me in a phone interview. The task was nearly impossible to do in places where the roads weren’t paved, let alone properly marked.
Having grown up in suburban New Jersey and now living in Boston, Ort wasn’t sensitive to how autonomous systems could impact rural communities until he started doing testing in rural Massachusetts.
As he learned, seasons change, and so do maps. Ort remarked that foliage on roadside trees could change enough to require a new map be created for autonomous cars.
“Maintaining detailed maps of large rural areas can be impracticable due to the rapid rate at which these environments can change. This is a significant limitation for the widespread applicability of autonomous driving technology,” Ort and his colleagues’ paper reads.
Ruralites stand to gain a bigger advantage from autonomous ride-hailing or car-sharing than city dwellers do, Ort told me, mainly because the carless in these areas have few transportation options; many small communities don’t even have public buses. A 2017 report by the American Public Transportation Association noted that seniors living in rural communities would prefer to “age in place” but that they need better mobility options to do so. A fleet of autonomous vehicles at a rural community’s beck and call could offer mobility options that don’t currently exist.
Once the technology is perfected, proponents argue that autonomous cars could also help improve safety on rural roads by reducing the number of impaired and drowsy drivers, eliminating speeding, and detecting and reacting to obstacles even on pitch-black roads. (A lot of work still needs to be done there.)
Ort’s algorithm isn’t commercializable yet; he hasn’t yet tested his algorithm in a wide variety of road conditions and elevations. Still, if only from an economic perspective it’s clear repeatedly visually capturing millions of miles of roads to train cars how to drive autonomously isn’t going to be winning mapping technology for AVs; it’s just not feasible for most organizations.
Whether it’s Ort’s work, or end-to-end machine learning, or some other technology that wins the navigation race for autonomous vehicles, it’s important to remember that maps are first and foremost a visual tool to aid sighted people in figuring out where to go. Like humans, a car may not necessarily need to “see” to get to where it’s going—it just needs to sharpen its other senses.
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