Like it or not, autonomous cars are hitting the road. One of their main selling points is that they will be safer than human drivers when it comes to avoiding accidents. It's true that human drivers aren't that great at avoiding motorcyclists. Can autonomous cars do better? Companies are only just starting to test them on open public roads, but so far the results are less than promising.

As we previously reported, there are at least two recorded cases so far of an autonomous car crashing into a motorcycle. On July 27, 2016, a motorcyclist in Norway was seriously injured when she was rear-ended by a Tesla Model S with its Autopilot self-driving system engaged. Additionally, on December 7, 2017, California motorcyclist Oscar Nilsson was hurt in a collision with a self-driving Chevy Bolt. The car had aborted a signaled lane change and was returning to its original lane while Nilsson was lane splitting past it, a maneuver that is legal in California. The car failed to detect Nilsson and lightly bumped him, causing his bike to fall over.

Another disturbing incident occurred when an autonomous Uber car struck and killed Elaine Herzberg, who was walking her bicycle across the road at night. One would think that the car's radar and infrared scanners would detect such an object far better than the human eye could. Indeed, the car's onboard computer detected the obstruction in the road six seconds before impact. Unfortunately, it failed to identify what type of object she was until just 1.3 seconds before impact, at which point it was too late to avoid hitting her. Other factors, such as an inattentive human driver who was supposed to be monitoring the car's surroundings, also contributed to the fatal crash, but a computer that fails to identify a target for nearly five seconds should be quite disturbing to motorcyclists who may suffer from this fate as well.

This trend isn't limited to autonomous cars, either. The example of the Tesla is a car that is not autonomous but is common on our roadways today. Adaptive Cruise Control is a radar guidance system that enables a car to change its speed relative to the vehicle in front of it based on traffic conditions. A study by RDW has determined that existing adaptive cruise control systems often do not do an adequate job of locking onto a motorcycle rather than a car. These systems seem to have difficulty detecting a motorcycle not riding in the center of its lane. This is a problem since riders tend to occupy either the left or right tire grooves of a lane, rather than the center, to avoid oil spills, road kill, and other hazards that car tires tend to push to the center of the lane.

Another study by Dynamic Research shows similar results when it comes to forward collision warnings detecting motorcycles. Throughout the tests, forward collision warning systems failed to detect the motorcycle adequately in 40 percent of trials. This 60 percent failure rate isn't too far off the 77 percent rate of human driver/motorcycle accidents occurring within a 60-degree arc of the front of the car cited in the famous Hurt Report. Similarly, 37 percent of simulated crashes in this test occurred because the car's onboard detection systems didn't see the motorcycle, which is also the number one cause of motorcycle accidents with human drivers.

As a result of this, the American Motorcyclist Association wants to get involved on the ground floor of autonomous driving system design. "The AMA wants the technology to be developed with us and with motorcyclists in mind, rather than deploying the technology, then trying to work motorcyclists into the picture," Michael Sayre, On-Highway Government Affairs Manager for AMA, told RideApart. "The AMA has reached out to manufacturers, systems developers, elected officials, and regulatory agencies with that request."

If autonomous driving systems are designed properly, keeping motorcyclists in mind, they could potentially deliver on their promise of making the roads safer for everyone. They could be on the lookout for motorcycles and other road hazards in all directions at all times, something even the most attentive human driver can never do. The trick is to design such systems to work in this way as a core functionality. Autonomous vehicles must be able to search, evaluate, and execute the same way the Motorcycle Safety Foundation teaches all riders to. This is especially true when it comes to detecting motorcycles, which don't have a steel cage around them to protect them from autonomous cars' mistakes.