UC researchers enhance training for snow and ice drivers

The problem with the traditional method of training – where experienced drivers train new drivers out on the road – is the limited number of resources. There are only a small number of experienced drivers, and each time you undergo a traditional trainer-trainee session, you’re using two drivers for one vehicle. Furthermore, experienced drivers may not always be the best trainers.

“We’re evaluating the behavior of older, experienced drivers,” says Ma. “Sometimes they become complacent, which may cause danger.”

Additionally, these training sessions are not always realistic. It’s hard to mimic the stress of a long work shift in a training session, and when roads are bad, potential instructors are typically out on road, not training other drivers.

Overall, there just are not many opportunities for real-world training for new, inexperienced drivers. Ma and Wang are looking for ways to change that. One way to do that is through driving simulation. 

The goal for this project is to provide recommendations on ways to enhance the snow and ice driving experience. To start, Ma and Wang are conducting an extensive evaluation of existing training methods. After they review standard practices, they will turn to what other states are doing, assessing the national technological landscape for ice and snow driver training. Then, they will bring their engineering backgrounds into the mix.

Ma’s expertise is in traffic management and driving simulation, whereas Wang is interested in behavioral tendencies. The team has a driver simulator at UC where they will apply much of what they’ve learned over the course of their research. 

The idea is to have similar simulators stationed across the state, so that new drivers can get trained in a realistic, albeit simulated, environment. When drivers are hired, they will use these simulators to prepare them for the real thing.

Getting to the point of a realistic simulated environment, however, will take a lot of work. Wang is currently in the process of monitoring drivers’ behaviors behind the wheel.

Students of Wang are riding along with drivers on their routes and documenting the actions required for them to operate the snow vehicles. They are also using GoPro cameras to record where drivers are looking, what equipment they are using and the frequency they are using these items. 

“For example, if we find the handles in the vehicle are very important, that means we need to put these items as hardware in this truck simulator,” says Wang.

Furthermore, Wang has found that the force of the vehicle will greatly impact the realism of the simulator, so it is important to account for the different forces a driver feels depending on the terrain.

“Overall, we’re determining which action is important and what types of functions are included and involved in the process of driving these trucks,” says Wang. “We will then use this information to complete the existing and potential simulators.”  

Both Ma and Wang agree that there will be plenty of challenges with creating the actual driver simulators. One major complication stems from the diverse snow and ice conditions of Ohio. The Ohio Department of Transportation is responsible for 43,000 lane miles of highways across the state. When these roads vary in geography, terrain and weather patterns, knowing how to maintain these roads becomes a tough proposition.

Cleveland and Cincinnati, for example, are over 250 miles apart. With this distance comes surprisingly great differences in snowfall.

Annually, Cleveland accumulates about 56 inches of snow, while Columbus and Cincinnati get 28 and 24 inches, respectively. It’s not uncommon for Cleveland to be hit by a blizzard, while Cincinnati remains untouched, perhaps even pleasantly temperate. Additionally, the weather varies so drastically from year to year that annually the department could employ as much as 900,000 tons of salt or as little as 300,000.

Ohio also differs significantly in geography across the state. Southwest Ohio is defined by a hilly terrain, which only dramatizes eastward into the Appalachian ranges of Southeast Ohio. Much of Central and Northwest Ohio, however, is flat. Northeast Ohio generally strikes a balance between the rough and hilly and flat and wide.

To account for these variances, Wang and Ma will have to create driving simulations that account for the diverse nature of the state-wide weather patterns. Once they assess these differences, as well as the current state of training methods, they can begin to build accurate simulations that reflect the realistic state of Ohio’s rough winter roads.

“We are going to try to create scenarios that are particular to the drivers’ environments, as well as their particular routes,” says Ma.

UC student Andrew Greer (computer science ’22) has been helping develop simulator capabilities in the lab all semester as part of his cooperative education (co-op) experience.

“I wanted something that got me out of my comfort zone, and this project is really unique,” says Greer.  

Though the research is still in its early stages, the team is looking to put the actual simulations in practice this February. They will use all the observations and assessments of data to determine as many variables as they can to create the ultimate simulation for drivers.

Thanks to Ma and Wang’s work at UC, regardless of fierce future winters, the state’s drivers will be more prepared to plow through whatever comes their way.  

Featured image at top: A truck with a plow clears a road of snow. Photo/Skentophyte/Pixabay