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Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon.

 

This paper presents a least squares formulation and a closed-form solution for identifying dynamical systems using irregular and sparse data obtained by chronologically merging measurements taken by multiple slow sensors of different sampling rates. We provide the theoretical foundation for developing advanced least-squares-based system identification algorithms for cases where the input-output data are asynchronous and/or scarce. Demonstrative examples are provided to validate the proposed method, and indicate the potential of removing the Nyquist sampling limitation in system identification. We provide in details how using 19 percent of the full measurements enables to capture the dynamics of a dynamic system when two slow sensors are collaboratively collecting the system response at different speeds. The required measurements can be further reduced under the proposed collaborative sensing scheme.