Autonomous Driving Risk Assessment With Boundary-Based Environment Model

Risk assessment is important for intelligent vehicles to make safe driving decisions. In some researches, the risk is modeled as the effect of each environment element on the ego vehicle. However, the overall understanding of the whole scenario is not achieved. In order to get a comprehensive understanding of the overall scenario, some researchers analyzed risk with 2D field. However, the analysis of surface-level dense risk distribution has heavy computational burden. In view of this, we proposed the boundary-based risk assessment method and corresponding decision-making mechanism, as a light-weighted way of understanding the overall risk distribution for intelligent vehicles. First, different environment elements are generally modeled as the unified environment boundary representing space occupation, semantic type and motion status. Then, the risk in different directions is analyzed according to the environment boundary in the ego-centered polar coordinate. Furthermore, considering the perception uncertainty, the safe boundary is set to serve for decision-making as boundary constraint. Experiment results proved that the proposed boundary-based risk assessment method has significantly reduced computational burden than surface-level dense risk assessment with similar decision performance.