United States Department of Transportation
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Edition:Final Report
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Abstract:Autonomous ground vehicles must safely operate in highly interactive environments with human uncertainties. Safe actions depend on context, interactions, and absolute (mathematical measures for safety) may differ from how humans perceive as safe and reliable behaviors. However, producing context-dependent and interaction-aware safe actions is non-trivial due to the following technical challenges. Challenge 1: Hardness in the identification of interaction mechanisms. When interaction mechanisms are known or can be learned, many safe learning and control techniques can be employed. However, in many interactive environments, it may be fundamentally difficult to fully identify the mechanisms of the opponent's interaction due to unobserved confounders. Challenge 2: Latent risk and latent variables. There exist latent risks (such as occlusions) and unobserved variables (e.g. awareness and intention), and safe actions depend on such factors. For example, pedestrians can decide to enter a crosswalk, but such intent may not be directly observable. Decisions that ignore such factors may experience unexpected risks. Challenge 3: Tensions between long-term safety vs. computation. Accounting for long-term outcomes in risk quantification and control is challenging due to stringent computation vs. time-horizon tradeoffs, particularly for rare events. Myopic safety can be efficiently certified, but ensuring long-term safety may require prohibitive computation.
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