An Interdisciplinary and Probabilistic Treatment of Contemporary Highway Design Standards

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Virginia Tech


Although Autonomous Vehicles (AVs) are quickly becoming a reality, there is much that needs to be understood before mainstream commercialization can occur. One critical issue is the interplay between multiple fields of engineering. Whereas the first part of this work is a granular treatment of a specific issue, the second part simultaneously examines numerous fields within the transportation industry. In the surge to understand and develop AVs, researchers tend to study specific subdivisions within the "vehicle engineering umbrella". In particular, mechanical and civil engineers study vehicle dynamics in two different levels of specificity. Mechanical engineers typically investigate small-scale dynamic behavior which applies to a single vehicle, such as vehicle-terrain interactions or the behavior of mechanical components. On the other hand, civil engineers tend to study kinematic behavior: the behavior of platoons as it pertains to large-scale traffic flow. Regardless of the scale of study, each subdivision has a set of performance metrics. Due to the differences among subdivisions, some performance metrics may (unintentionally) compete. Compromises must be made in the design stage to produce a vehicle which caters to an appropriate audience.

The first part of this work features two major contributions to bridge the gap between the dynamic and kinematic perspectives. One is the application of Design Envelopes that establishes a framework to balance constraints and assess design tradeoffs arising from each viewpoints. Three Design Envelopes are introduced to reach compromises on a vehicle's velocity, acceleration, and jerk. Another contribution is a methodology to tune the parameters of a car-following model analytically. Current tuning practices require empirically collected traffic count data, which is cumbersome to obtain. Analytically parameterizing car-following models facilitates more robust planning and encompasses both the dynamic and kinematic perspectives. The second contribution utilizes these Design Envelopes to improve a currently-existing speed profile generator. Integrating the Design Envelopes reformulates the existing algorithm as a constrained LQR problem, which enhances ride comfort and maintains dynamic stability for not just one vehicle, but a platoon. Simulations demonstrate that the refined algorithm can reduce the travel time on a specific route by 3-4.4%. More importantly, the simulations demonstrate it is possible to synthesize multiple engineering fields to enhance AV design.

The second part of this work features two contributions aimed at revisions to modern-day highway design policies based on the concept of combining microscopic and macroscopic principles. One common belief is that AVs should drive better than the best human drivers, which suggests operating at or close to the vehicle's theoretical handling limits. Operating in this manner requires a thorough understanding of the associated risks, particularly the risks stemming from uncertainty. This is especially pertinent as there are many inherently probabilistic quantities that are conveniently treated as deterministic in vehicle performance simulations, such as the coefficient of friction. This is a questionable practice when operating on the precipice of compromised safety. Thus, the second part of this work probabilistically examines the chance of handling loss given the amount of tire-road friction and driver acceleration. The result is a mathematically rigorous quantification of a safety margin for various road conditions and driver ability levels. Changes to the official US highway design handbook are recommended based on the findings.



Reliability Analysis, Green Book, Performance Margin, Highway Design, AASHTO, SAE