Autonomous vehicles: Are they ready for road challenges yet?
I was at CES in Las Vegas this year and managed to get a ride on Inducts driverless vehicle Navia on a closed track, traveling at 12.5 mph. That’s pretty safe. I’m not sure I’m ready to relinquish my control as a driver in my car yet to software, high tech LIDAR and cameras.
Commercial aircraft has been flying with auto-pilot and autonomous approach and landing software for a while, but they don’t have to deal with pedestrians darting out in the roadway, the driver in the next lane who spilled their coffee and is swerving into you or the person in the car behind you texting who does not see the traffic slowing down (texting, looking in the mirror, on the phone, etc.) and is quickly approaching your rear bumper about to be “bumped”. I love the way Nuvation’s CEO, Mike Worry, looks at the issue and would make it illegal for humans to drive cars. I have to agree.
On the bright side, there are many ongoing developments in software and electronic controls that are very promising to autonomous vehicle safety and ultimate implementation. Let’s take a look at some of these efforts.
University of Waterloo
Steven Waslander and Nuvation have made a good team in the autonomous vehicle circuit with their articles and test track platform to prove out theoretical schemes. Mike Worry, a graduate of the University of Waterloo's Electrical Engineering program, has a four-year research agreement with the University of Waterloo to identify new products for autonomous vehicles.
The importance of Autonomous Vehicle tire dynamics1
Autonomous vehicle dynamics are critical to the safety or passengers and pedestrians and other vehicles on the road. Those dynamics, used by controllers in autonomous vehicles, need to be well understood and rigorously tested in order to declare an autonomous vehicle safe. Some recent controller designs are making an effort to operate the vehicle close to the tire friction limits in order to maximize vehicle performance.
The tire/road forces and their interaction play a big part in Autonomous Vehicle dynamics. The Slip Circle (Figure 1) shows the maximum force generated by the tire.
Figure 1: The Slip Circle shows the horizontal axis as the normalized sideslip angle, and the vertical axis as the normalized longitudinal slip ratio. Point A is a high slip ratio and a low slip angle which represents the situation when the vehicle accelerates. Point B is a low slip ratio and a high slip angle which represents the situation when the vehicle steers aggressively. The dotted lines represent the vehicle driving right at the limits of friction. (Image courtesy of Reference 1)
Research teams have tried to estimate the slip circle parameters because the slip angle and longitudinal slip predict vehicle dynamics. They have found that slip angle is able to be calculated via measurements by accurate GPS and Inertial Measurement Units (Defined by Xsens, recently acquired by Fairchild). The problem here is that this method is highly sensitive to noise and low-cost sensors on existing commercial vehicles will not work well. This prompted researchers to consider estimation/observer algorithms. See Figure 2.
Figure 2: Observer diagram with Pneumatic Trail Estimator (Image courtesy of Nuvation)