Weather conditions
Complex environment and traffic conditions have a major impact on the safety and operations of Connected and Automated Vehicles (CAVs). Weather affects not only the vehicle performance but also the roadway infrastructure, thereby increasing the risk of collision and traffic scenario variations.
So far, most automated vehicles have been primarily trained and tested under optimal weather and road conditions with clear visibility. However, the systems must prove that they are equally reliable and accurate under any weather and road condition before they can see widespread acceptance and adoption.
The challenges for automated driving systems caused by harsh weather conditions, such as fog, rain and snow are substantial, as these affect the functioning of their key technologies and their development: sensors, detection, control and system testing.
There is currently a strong push globally for automated vehicles in general and towards solving harsh-weather-related challenges.
Harsh weather challenges are
a severe technological barrier
for automated vehicles.
ROADVIEW's solutions
- Adaptive sensor fusion to select the optimal cost-effective multisensory setup
- Early sensor noise filtering to improve detection of objects and vulnerable road users
- Inclusion of collaborative perception for increased robustness
- Mathematically grounded sensor noise modelling to represent the variations in harsh weather characteristics and thus generate realistic synthetic sensor data
- Acceleration of testing by simulation-assisted methods, including the development of digital twins of controlled and real-world environments
