# Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions
## 4 Discussion  
In conclusion, all tested LiDARs performed worse in fog and turbulent snow than in clear weather conditions, as expected. In fog chamber tests, every sensor's performance decreased the denser the fog and the further the target. Making a direct comparison, Cepton's different approach proved the most efficient in stationary fog chamber tests but its point clouds are quite noisy. No significant differences were found between the other sensors. The darker target was more difficult for the sensors to detect than the lighter one.  
In the turbulent snow tests, all tested sensors were blocked by the powder snow and their viewing distance was shortened. However, from these tests, it is not possible to say if some sensor performed absolutely better than the others did. The powder snow itself was not visible in two of the sensors' data, but its presence was observable by the missing echo points in front of and behind the test vehicle. Only Robosense produced points that directly showed the presence of snow. It is notable that not only does the leading vehicle cause the snow to whirl, but the test vehicle itself does as well. This makes detection in that direction difficult as well and shortens the viewing distance. Temperatures that were a few degrees centigrade below zero did not affect the performance of the LiDARs. However, based on these tests we cannot say how well they would perform in even colder environment.  
There are no ideal fog, rain, or snow conditions that create a single threshold when the objects are not visible anymore. Moreover, this is highly dependent on LiDAR type. For example, with multilayer LiDAR (e.g., 16 or 32), the influence of one single laser spot is less significant. However, in the future, the aim is to investigate if the parameters and reliability levels of object recognition can be more precisely adapted according to density of the atmospheric changes. This would be essential, especially when considering sensor data fusion and having an understanding which sensor data the automated vehicle should rely on. At this level of LiDAR performance, fully autonomous vehicles that rely on accurate and reliable sensor data cannot be guaranteed to work in all conditions and environments. The powder snow on the roads is very common in colder environments for much of the year and, thus, is not ignorable. The cold temperatures may also bring other difficulties to the used sensors that are, as yet, unknown to us. This provides encouragement for the continued investigation of the LiDAR performance in these challenging conditions.  
[PERSON] is primarily responsible for planning the test scenarios and executing the analysis of range degradation in various weather conditions. In addition, she was also in charge of detailed analysis and reporting the key findings. [PERSON] contributed to the article by identifying the automated driving requirements in various weather conditions. He planned the test scenarios and analyzed the pros and cons of the various automotive LiDARs. [PERSON] has been the main software developer for programming analysis tools and gathering data in various weather conditions. He has also been in charge of analysis of the data.  
This research was funded by the DENSE-EU-ECSEL project (Grant Agreement ID: 692449). DENSE is a joint European project which is sponsored by the European Commission under a joint undertaking. The project was also supported by Business Finland and other national funding agencies in Europe.  
We would like to thank the DENSE-EU-ECSEL project and the Forum for Intelligent Machines (FIMA) for collaboration in both fog and snow tests and the opportunity to use obtained data for this study.  
The authors declare no conflict of interest. The funders had no role in the design of the study; nor in the collection, analyses, or interpretation of data; nor in the writing of the manuscript or in the decision to publish the results.