Investment and financing of roadway digital infrastructure for automated driving
Bibliographic record
Abstract
Connected automated vehicles (CAVs) are equipped with sensors, enabling them to scan and analyze their surrounding environment. This capability empowers CAVs to make informed and efficient decisions regarding their motion; however, the limited spatial range and resolution of these sensors present challenges for achieving full autonomy. Cooperative sensing through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications offers an alternative approach to enrich CAVs’ environmental understanding. This study explores the optimal investment policy for vehicular connectivity and road-side sensor deployment under varying traffic flow conditions. It also extends the self-financing theorem to the sensor equipped roads and investigates whether an optimal toll can cover both the construction costs and the costs of equipping roads with sensing components. The stylized model of CAV mobility considers the interplay between stationary sensors installed road-side as a part of the infrastructure and mobile sensors of CAVs. Results indicate that under constrained budgets and low traffic flow, investing in infrastructure improvement is preferred. However, as traffic flow increases, prioritizing connectivity and data sharing among CAVs becomes more lucrative. Notably, in high traffic flow, a shift back to investing in stationary sensors may occur, depending on system settings. The findings provide insights into budget allocation to enhance CAV performance, advancing the development of efficient and safe automated driving systems. The analyses on the self-financing theorem also show that the optimal user tolls do not cover the cost of constructing digital infrastructure. However, if social planners consider the safety benefits of sensor equipped roads, the construction of digital infrastructure can be covered by the optimal user tolls. In addition, the revenue from optimal user tolls can cover the cost of equipping existing roads with sensors if their flow-capacity ratio is greater than a certain threshold. • We investigate optimal investment strategies in vehicular connectivity and road-side sensors. • For a given budget and low traffic flow, investing in roadside sensors is preferred. • For high traffic flows, investment should be allocated more to vehicular connectivity. • In high traffic flows, a shift back to investing in stationary sensors may occur depending on certain conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".