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The acid test for autonomous driving algorithms in the North

Checklist for winter-proof autonomous car development

Arto Makkonen / February 19, 2019

In Scandinavia, it has been a spectacular winter for skiing, skating, ice hockey – in fact, any winter sports or outdoor activities. However, if you are tasked with developing robust artificial intelligence (AI) algorithms, car sensors or other measurements for autonomous cars, circumstances have been extremely challenging.

My own 6-year old Sedan is far from autonomous, but it has a parking sensor. This winter, the parking sensor was not functioning for at least 5 weeks in a row, which means I've been re-practicing reversing the car in full manual mode, like in the good old days.

Teaching the driving AI what is different in winter

In winter, car controls remain the same, but the driving AI must consider many factors impacting driving experience and safety:

  • Visibility range or sensor range can be poor or even temporarily disappear completely. There can be a flurry of snow, layers of ice and frost on windows and sensors.
  • The dynamic response of the car when accelerating, braking or turning is very different on icy roads: braking distance varies a lot in different conditions and may be hard to predict. Acceleration must be done carefully and sometimes it is necessary to switch off the traction control system to get moving.
  • You have to accept that the driver might not be able to drive on the lanes. Snow may block part of the lane, and road maintenance vehicles frequently clean a new track slightly off the lanes. Also, the traffic forms new 'best tracks' on the snowy road.
  • The driver can't see the edge of the road, yet he needs to keep the distance to it to prevent the car from spinning.
  • The roads can be very bumpy because of thick ice formations, especially between the wheel tracks. The driver needs to avoid the problematic ice formations but also know the difference between normal and problematic jolts of the car.

New sub-industries enter autonomous driving markets

I visited the Consumer Electronics Show (CES) 2019 in Las Vegas; an event which has grown into one of the leading automotive technology events. Vendors were showcasing their core autonomous driving solutions and roadmaps: Big data giants like Baidu and Google, silicon vendors such as NVIDIA, Qualcomm and Intel's Mobileye, car OEMs like Mercedes, Porsche, and Nissan, to name a few.

Autonomous driving has clearly entered a very practical phase. The LiDAR ecosystem included companies that are specializing in cleaning nozzles and spray systems for the car sensors. There were vendors making transparent heaters that can be applied to keep the sensors free from frost and snow.

Checklist for winter-proof autonomous car development

1. Weather and environment proof sensor placement. In today's cars, the windscreen can be kept clear in most weather conditions. These solutions can be reused for example by placing sensors in the windscreen area or by creating sensor cluster casings with familiar defrosting and washing solutions.

2. Keeping sensors clean and frost-free. This includes all new washing solutions, wipers, nozzles, spray systems, resistive heaters, and fans.

3. Monitoring and adapting to sensor conditions. It is necessary to monitor and diagnose whether the sensor is delivering high-quality measurements, and if it is not, the AI needs to support limited sensor driving modes using fewer measurements.

4. Vehicular communication systems. Vehicles have started communicating with other vehicles (V2V) and any objects around them (V2X). With this technology, cars will be able to announce their position and speed in real time and start "seeing" their environment independently of sensor measurements.

5. The new generation of real-time digital maps. Today, cars are getting new kind of map information: these maps are designed to be machine readable and include centimeter-accurate details of the roads, objects near the roads and other location data. Together with accurate outdoor and indoor positioning, these maps help cars navigate every corner and every small turn. Recently, our strategic partner around location-intelligence, HERE Technologies, shared insights about digital maps.

6. Enough data and the right data for training. Driving is complex. There simply is no AI solution that can generalize far beyond the data used for training. There must be enough (= lots of) representative data from all road conditions. Different AI models are needed depending on the weather conditions.

7. Taking the driver's seat is enough. As AI increases its capabilities, it is supposed to perform better than humans in more and more tasks. One day, driving will belong to one of these tasks: if humans can drive in winter conditions only by sensing the 360-degree view from the driver's seat, audible information, and acceleration of the seat, then AI should be able to do the same.

I'm confident that drivers and passengers can soon experience riding autonomous cars whilst enjoying movies, games or extra work time. However, it seems that one job remains for humans in winter conditions: brushing the snow off the car and scratching the ice off the windows before starting the engine.

Read more about Tieto's software engineering services for the Automotive industry.

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Arto Makkonen
Sales Director, Tieto

Arto has deep experience in product software development. He has managed several mobile, connected devices, and digital services projects. Today, he focuses on Automotive and Smart Products businesses.

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