How AV developers use virtual driving simulations to stress-test adverse weather - The Robot Report

The article discusses the significant challenges adverse weather conditions pose to autonomous vehicle (AV) systems, highlighting that rain, snow, fog, glare, and varying road surfaces can severely distort sensor inputs and decision-making processes. While AV technologies have advanced in ideal conditions, real-world environments with bad weather introduce complex disruptions that traditional training data often fail to address. Each sensor type—cameras, lidar, and radar—faces unique vulnerabilities: cameras suffer from obscured vision and noise, lidar can be affected by precipitation scattering laser beams, and radar, despite better penetration through fog and rain, experiences reduced resolution and clutter. When multiple sensors degrade simultaneously, overall system performance deteriorates sharply. These sensor challenges lead to perception and prediction failures, where objects may be missed or misclassified, and behavioral predictions become unreliable due to altered pedestrian and vehicle behaviors in bad weather. Such failures can cascade into unsafe planning and control decisions by the AV. Real-world incidents have demonstrated AV prototypes disengaging or misbehaving in adverse weather,