Ford Files Patent for System to Detect Lane Splitting Motorcycles

Ford Files Patent for System to Detect Lane Splitting Motorcycles

Author: Eric Walz   

California is often associated with is notoriously bad traffic, especially in the Los Angeles area San Francisco Bay areas. Despite its traffic woes, the state allows motorcyclists to squeeze between cars in a practice known as "lane spitting", or driving past stopped traffic in the space between two lanes.  

California is an exception, it is the only state in the U.S. that allows this practice, simply because there is no law prohibiting it. Lane splitting is also legal in Europe and Asia.

For a self-driving car to successfully navigate through vehicle traffic, the car needs the capability to avoid motorcycles riding between the lanes. Ford Motor Company was recently granted a patent to address this problem. The technology was developed at Ford's Silicon Valley Research & Innovation Center in Palo Alto.

ford mc.JPG

The automaker's patent is for a system that uses cameras and other sensors to detect lane-splitting motorcyclists and intervene, to possibly avoid a collision. The patent calls for using rear-facing cameras that would supplement a vehicle's advanced driver assist systems (ADAS), such as blind-spot detection to issue a warning and initiate automatic braking or steering if a motorcycle is detected in the space between lanes.

Ford's lane-splitting patent can be adopted using existing technology found on cars today.

Since the patent uses existing sensors and other electronics equipped on cars today, it would not take years to implement in production vehicles. In addition, since ADAS are becoming common on most entry level vehicles, the feature could rapidly proliferate throughout automakers' fleets.

For motorcyclists, lane-splitting is regarded as a safety practice, to avoid the space between two cars in case of a collision from behind. This danger increases at intersections where traffic is often stopped, which is why many bikes use the free space between lanes when stopped at red lights. However, the practice remains dangerous. Other motorists cannot see these bikers is adjacent lanes, especially when lanes are merging together.


A motorcyclist lane splitting

Self-driving cars have an even more difficult time than humans detecting small, fast-moving objects. Or even small, slow-moving objects around the vehicle.

This was the case in March when a self-driving Uber vehicle fatally struck a pedestrian walking her bicycle across a street in Arizona. Uber's software is being blamed for not identifying and stopping for the pedestrian in the roadway.

A recent IEEE Spectrum report revealed that slower moving bicycles are "the most difficult detection problem that autonomous vehicle systems face." this problem is compounded for motorcycles, as lane-splitting motorcycles often move much faster than a bicyclist.

Is is not clear if Ford's new patent will ever reach production vehicles. However, Ford's already has Pedestrian Detection technology as part of its Pre-Collision Assist feature. The system can conceivably be redesigned to spot motorcyclists who choose to split lanes using existing cameras.

Externally-mounted cameras are a common feature on newer vehicles to help eliminate blind spots for the driver. Rearview backup cameras are already mandated on all 2018 and newer models in the U.S.

Until there ar level 4 and 5 autonomous cars on the roads, a human driven is still relied on for backup of today's safety systems. While all of this technology may be extremely helpful in unexpected situations, it does not replace an alert driver always on the lookout for motorcycles.

Eric Walz
Eric Walz
Originally hailing from New Jersey, Eric is a automotive & technology reporter covering the high-tech industry here in Silicon Valley. He has over 15 years of automotive experience and a bachelors degree in computer science. These skills, combined with technical writing and news reporting, allows him to fully understand and identify new and innovative technologies in the auto industry and beyond. He has worked at Uber on self-driving cars and as a technical writer, helping people to understand and work with technology.
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