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Autonomous vehicles commonly include light detection and ranging (LiDAR) scanners in their suite of sensors. LiDARs are usually mounted on the vehicle’s roof to generate a point cloud of surrounding surfaces. In winter driving, falling snow casts randomly distributed shadow areas in the path of the LiDAR laser beam. This causes the scene to be obscured from the sensor to a degree proportional to the rate of snowfall, and snowflake size. In this paper, a post-processing model is developed for simulating the effect of synthesized snowfall on surrounding vehicle detection accuracy. This additive noise filter synthesizes the effect of falling snow in LiDAR data based on laser ray path and is applied to data from the popular, clear weather, KITTI road driving dataset. Object detection accuracy was quantified using metrics developed to study this effect, the mean and standard deviation of detections bounding box centroid error, the percentage error in detection bounding box volumes and the percentage of hidden original scene points in the shadow of synthetic noise points. These are important metrics an autonomous car needs to safely interact with other detected vehicles on the road. Object correlation between normal and noisy frames has been used to ensure the accuracy of the metrics as the noise introduced to the point cloud alters the number of detections. The simulation results show the effect of synthesized noise on the number and location of detections in each frame. The effect can be seen as lost detections in some cases. In others, it is present by introducing false positive detections in the scene. The testing at various noise levels also shows an increasing detection centroid mean error and bounding box volume percentage error with increasing noise. As the noise level increases, the point cloud in a frame grows with reflections of the synthesized snowflakes; however, the percentage of covered original LiDAR points shadowed by snowfall remain almost constant at a mean percentage value of 0.24%.
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