Space Distribution Method for Autonomous Vehicles at a Signalized Multi-Lane Intersection
Bibliographic record
Abstract
Under the connected vehicle environment, autonomous vehicles (AVs) could bring numerous advantages including: improving the traffic flow, enhancing safety and alleviating air pollution. However, optimally operating AVs at signalized multi-lane intersections is a challenging problem due to the complex interaction of vehicles between lanes. It is thus a desire to manage and control the dynamics of AVs at signalized multi-lane intersections. To this end, this paper puts forward a bi-level control framework to optimize the intersection throughput. In our proposed method, the upper level (i.e. the intersection controller) is used to optimize the lane usages of each approach and the AVs' positions. In contrast, the lower level (i.e. the vehicle controllers) receives information from the upper level to control the AVs to get the maximum speed. More specifically, in the upper level, we apply a novel Space Distribution Method (SDM) for the AVs to maximize the throughput (i.e. a number of AVs) of the (multi-lane) intersection where signal timings are predefined. The SDM is divided into three steps: i) platoon formulation; ii) lane-mode optimization; and iii) AVs' position distribution. To maximize the throughput, the intersection controller receives information about the states of the AVs (e.g. the trajectories), then optimizes the lane usages for each approach, the desired speed, and the gap of the AVs as well as the AV's position along the approach. After that, each AV which is allowed to cross the intersection will determine its own trajectory and travel with the scheduled time without crash. Numerical simulations are set up to show that the throughput increases significantly, even more than twice of the throughput obtained from other methods in some circumstances.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".