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  1. Free, publicly-accessible full text available December 29, 2024
  2. Free, publicly-accessible full text available January 1, 2025
  3. 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.

     
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  4. 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. 
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  5. Abstract

    We present a system identification method based on recursive least-squares (RLS) and coprime collaborative sensing, which can recover system dynamics from non-uniform temporal data. Focusing on systems with fast input sampling and slow output sampling, we use a polynomial transformation to reparameterize the system model and create an auxiliary model that can be identified from the non-uniform data. We show the identifiability of the auxiliary model using a Diophantine equation approach. Numerical examples demonstrate successful system reconstruction and the ability to capture fast system response with limited temporal feedback.

     
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