Self Driving Cars

   
Cars that drive themselves are now everywhere. They’re prowling the streets of California and Michigan, Paris and London, Singapore and Beijing. Waymo, the company that emerged from Google’s self-driving car project, has been at it the longest, but its monopoly has eroded of late and tech giants like Apple, Intel, and IBM are looking to earn their share of profits. But how did self-driving cars even come to be, and how do they work?

The First Self-Driving Cars
The Urban Challenge, the third and final competition for autonomous vehicles put on by Darpa, or The Defense Advanced Research Projects Agency. At the time, America’s military-industrial complex had already spent millions of dollars and many years of research trying to make unmanned trucks. This laid a foundation for the technology, but slowed when it came to making a vehicle that could drive at practical speeds, through all the hazards of the real world. So, Darpa invited the whole world to build a vehicle that could drive across California’s Mojave Desert, and whoever’s robot did it the fastest would get a million-dollar prize.


The 2004 Grand Challenge did not end well. Each team grabbed some combination of the sensors and computers available at the time, wrote their own code, and welded their own hardware. The most successful vehicle went just seven miles. A majority of the others crashed, flipped, or rolled over within sight of the starting gate. But the race created a community of people who believed the robot drivers people had been craving for nearly forever were possible.

They came back for a follow-up race in 2005 and proved that making a car drive itself was possible: Five vehicles finished the course. By the 2007 Urban Challenge, the vehicles were not just avoiding obstacles and sticking to trails but following traffic laws, merging, parking, even making safe, legal U-turns.

When Google launched its self-driving car project in 2009, it started by hiring a team of Darpa Challenge veterans. Within 18 months, they had built a system that could handle some of California’s busiest roads with minimal human involvement. A few years later, Elon Musk announced Tesla would build a self-driving system into its cars. Ride services like Uber and Lyft weakened the link between being in a car and owning that car, helping set the stage for a day when actually driving that car falls away too. In 2015, Uber even hired dozens of scientists from Carnegie Mellon University to get its effort going.

After a few years, the technology reached a point where no automaker could ignore it. Companies like Ford, General Motors, Nissan, Tesla, and Mercedes started their own self-driving car projects. The tech giants followed, as did an armada of startups: Hundreds of small companies are now rushing to offer improved radars, cameras, lidars, maps, data management systems, and more.

How do they work?
A self-driving car is capable of sensing its environment and navigating without human input. To accomplish this task, each vehicle is usually outfitted with a GPS unit and a range of sensors including laser rangefinders, radar, and video.  The vehicle uses positional information from the GPS and inertial navigation system to localize itself and sensor data to refine its position estimate as well as to build a three-dimensional image of its environment.


Data from each sensor is filtered to remove noise and often fused with other data sources to augment the original image. How the vehicle subsequently uses this data to make navigation decisions is determined by its control system.

Obstacle Avoidance

A vehicle’s internal map includes the current and predicted location of all static (e.g. buildings, traffic lights, stop signs) and moving (e.g. other vehicles and pedestrians) obstacles in its vicinity. Obstacles are categorized depending on how well they match up with a library of pre-determined shape and motion descriptors. The vehicle uses a probabilistic model to track the predicted future path of moving objects based on its shape and prior trajectory. For example, if a two-wheeled object is traveling at 40 mph versus 10 mph, it is most likely a motorcycle and not a bicycle and will get categorized as such by the vehicle. This process allows the vehicle to make more intelligent decisions when approaching crosswalks or busy intersections. The previous, current and predicted future locations of all obstacles in the vehicle’s vicinity are incorporated into its internal map, which the vehicle then uses to plan its path.

Path Planning

The goal of path planning is to use the information captured in the vehicle’s map to safely direct the vehicle to its destination while avoiding obstacles and following the rules of the road. Although manufacturers’ planning algorithms will be different based on their navigation objectives and sensors used, the following describes a general path planning algorithm which has been used on military ground vehicles.

This algorithm determines a rough long-range plan for the vehicle to follow while continuously refining a short-range plan (e.g. change lanes, drive forward 10m, turn right). It starts from a set of short-range paths that the vehicle would be dynamically capable of completing given its speed, direction and angular position, and removes all those that would either cross an obstacle or come too close to the predicted path of a moving one.  For example, a vehicle traveling at 50 mph would not be able to safely complete a right turn 5 meters ahead, therefore that path would be eliminated from the feasible set.  Remaining paths are evaluated based on safety, speed, and any time requirements. Once the best path has been identified, a set of throttle, brake and steering commands, are passed on to the vehicle’s on-board processors and actuators. Altogether, this process takes on average 50ms, although it can be longer or shorter depending on the amount of collected data, available processing power, and complexity of the path planning algorithm.

The process of localization, mapping, obstacle detection, and path planning is repeated until the vehicle reaches its destination.
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