The goal of the project is to reduce road accidents by making autonomous vehicles more capable at operating in winter conditions, such as found in Norway. The current generation of self-driving cars, or driver assistant systems, do not contain the intelligence that is required to recognize slippery roads. Most cars that are sold in Norway today contain Advanced Driver Assistance Systems (ADAS) that aim to reduce road incidents. Examples are Autonomous Emergency Braking (AEB) and Lane Keeping Assist (LKA). However, a notorious limitation is that such systems do not function properly in winter conditions, and under certain circumstances can even increase the chance of serious incidents. As a consequence, it is believed that introducing self-driven cars on winter conditions will increase the risk of fatalities, when compared to only having (experienced) human drivers. It is believed that by providing these autonomous systems with the intelligence to determine the slipperiness of the road, the performance can greatly be improved, and the risk of fatalities can be reduced.
The primary objective of the proposed study is to develop an accurate and affordable method for road-friction estimation in real-time. Such estimates are established using a novel combination of road weather models and car-mounted environmental sensors. Besides improved road safety, the proposed technology offers the potential for environmental benefits through intelligent route planning, which can reduce CO2 emissions, and offer optimized winter road maintenance, which reduces the need for chemicals that are harmful to the environment.
Researcher
- Tromsø
fand@norceresearch.no
AutonoWeather: Enabling autonomous driving in winter conditions through optimized road weather interpretation and forecast
CONCLUDED
01.04.20 - 30.09.22
8.000.000 NOK
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