Global Car Brands, Kathleen
Global Car Brands, Kathleen
As Mazda and Infiniti have proved, there’s a lot of innovation left in the internal-combustion engine. One of the more wild concepts we’ve seen is called Reactivity Controlled Compression Ignition (RCCI), and it could just be the holy grail of internal combustion. Why? It uses gas and diesel to achieve incredible levels of efficiency.
This engine only lives on a test bench now, as Engineering Explained’s Jason Fenske details. It’s a concept developed by the University of Wisconsin-Madison that in lab testing has achieved 60 percent thermal efficiency. That means this engine is converting 60 percent of its fuel used into power rather than waste energy—a much higher number than any automotive engine in production today. For context, Toyota has a new 2.0-liter four-cylinder that achieves a remarkable 40 percent thermal efficiency, while Mercedes-AMG’s F1 power unit achieves 50 percent.
An RCCI uses two fuel injectors per cylinder to mix a low-reactivity fuel, like gas, with a high-reactivity fuel, like diesel. Theoretically you could mix any low- and high-reactivity fuels for RCCI to occur, but gas and diesel is perhaps the most interesting combination.
The combustion process is quite fascinating. First, a mixture of gas and air enter the combustion chamber, then diesel is injected. The gas and the diesel start to mix as the piston moves up towards top dead center, at which point, a little more diesel is injected for ignition.
This engine is more fuel efficient than a conventional diesel engine, and it’s much cleaner too. It sounds like the ideal internal-combustion engine, so that might make you wonder why it’s not yet in your car? Well, the engine is still very much in development, so if it ever were to enter mass production, it wouldn’t be for some time. There’s also the whole issue of having to equip a car with two entirely separate fueling systems.
But, it’s still a fascinating concept, and one that Fenske explores very in-depth in this video.
For the most part, self-driving car tests have focused on cities. Boston, Pittsburgh, Las Vegas and dozens of other cities have begun or plan on beginning self-driving pilot programs. There are reasons that companies look at cities. Cities generally have clear signage and well-maintained roads, two things that make the huge challenge of AI driving a little easier to break down and tackle.
Companies like Google or Uber also invest in complex 3D-mapping for these urban areas, labeling each and every road sign and lane. Given the complexity of the operation and the fact that millions of miles of U.S. roadsare unpaved, unlit, or unreliably marked, it’s no wonder the development focus so far has been decidedly urban.
That’s something MIT’s Computer Science & Artificial Intelligence Lab (CSAIL) wants to change.
Rather than relying on 3D mapping, MapLite focuses its efforts on satellite mapping like the kind you can find on Google Maps, and a series of LIDAR and IMU sensors.
The system then creates two goals: a final destination and a “local navigation goal,” in view of the car. The LIDAR then estimates where the road begins and ends. Without sign markings, the LIDAR makes basic assumptions that the road will be relatively more flat than the surrounding areas that are not road.
“The reason this kind of ‘map-less’ approach hasn’t really been done before is because it is generally much harder to reach the same accuracy and reliability as with detailed maps. A system like this that can navigate just with on-board sensors shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped,” says CSAIL graduate student Teddy Ort, who was a lead author on a paper about MapLite, in a press statement.
The system was tested around Devens, Massachusettes, a census-designated area in central Massachusettes with a population under 2,000. Driving a Toyota Prius around on multiple unpaved country roads, the CSAIL team was successfully able to self-drive and detect the road more than 100 feet in advance.
The system isn’t perfect yet. It’s best equipped for forest-type environments like those around Devens, it can’t handle a mountain road due to their dramatic changes in elevation. But the successful test is a first step, researchers hope.
“I imagine that the self-driving cars of the future will always make some use of 3-D maps in urban areas. But when called upon to take a trip off the beaten path, these vehicles will need to be as good as humans at driving on unfamiliar roads they have never seen before. We hope our work is a step in that direction,” says Ort.