With the break over Christmas and the New Year, we didn’t have time to write an update on planning for our second day of testing at UTAC on the 6th January so we’ve combined it with the debrief here.
Our Day 1 of testing in December went well but we had a lot of open questions about how to measure efficacy – perhaps too much of a goal for a first day but an area we hoped to be clearer on after Day 2. However with robust data collection and some tweaks to our Artificial Intelligence MVC, we were confident of being able to assess where value and benefit can be created with these types of driver interventions. Let’s discuss what happened!
Our co-founder Anna, setting the cars off on their laps
Session 1: app on (driver intervention)
Session 2: app off (baseline)
The afternoon continued to be dry (and sunny). With the MVC turned off, the phones were used to track the vehicles as they moved around the course. As discussed on the Day 1 debrief, with relatively low vehicle density and a good line of sight, human drivers are relatively good at navigating short, narrow stretches of road. Having 10 professional and experienced drivers in mid-sized and small vehicles also helped. The traffic in the 2 inner, narrow sections tended to move well with and without the software running.
However, the other 2 narrow stretches had less visibility, so when making a decision to proceed through the narrow section, the drivers were unable to see if another vehicle was approaching from the other end. Due to this, every time 2 vehicles entered from opposite ends they met and 1 vehicle needed to reverse. As subsequent cars entered the narrow stretch, a small traffic jam formed, as determining who should reverse became harder for drivers to communicate across several cars.
The professional drivers at UTAC (and Damian and Marcus)
Another important dynamic is reducing head-on meeting occurrences, which is about safety rather than time. Being able to see round corners is as much about reducing stress and improving safety than simply saving time.
Head-on occurrences will occur if 2 vehicles traveling in opposite directions enter a narrow stretch at the same time. We will need to investigate this occurrence on different roads to estimate how frequently this occurs, but we observed significant reduction in this risk in our testing when the app was intervening. The hope for the MVC software is that these high risk situations can be fully prevented, similar to when traffic lights are placed on small narrow bridges, but with a far lower infrastructure cost.
We are starting to plan for our next testing, which will be at Assured CAV (HORIBA MIRA) at the end of February. We will increase our testing to include 20 vehicles and include new road layouts.
We are also now exploring on-the-road use cases and are looking at events and festivals based in rural areas. Watch this space for more information!
A few modifications for the track setup at Horiba Mira are required. We will need to ensure the drivers can’t see ahead on the road before the narrow stretches, to truly replicate rural roads with ‘blind spots’. We will also need to split the efficacy assessment for time savings (often more than 2 cars) and head-on safety risk (2 cars).