Deep learning identifies dangerous roads from sound input

Every year wet pavement causes 74% of all weather-related crashes in the United States, with 384,032 persons injured and 4,789 persons killed. The problem of identifying when roads are dangerously wet, particularly in conditions which can obscure the fact, isn’t just one of public road safety in general, but is likely to be crucial in allowing new generations of self-driving cars to operate reasonably in ‘real world’ conditions without defaulting endlessly to inefficient slow speeds, handovers to operators or crisis-parking. Video-based detection of dangerously slippery road surfaces can be hampered by fog, poor light or other environmental conditions. Some systems have attempted to use the presence of road reflections of other drivers’ headlights in order to determine precipitation, either using fixed or on-board cameras; but the former are remote to…


Link to Full Article: Deep learning identifies dangerous roads from sound input