An Enhanced 3D Sensor Deployment Method for Intelligent Cooperative Sensing in Connected and Autonomous Vehicles
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Currently, heterogeneous driving scenarios and complex traffic conditions challenge connected and autonomous vehicles (CAVs) to achieve accurate sensing of road conditions. Existing research on the sensing capabilities of vehicles only relys on adding more onboard sensors, which makes the driving safety unable to be guaranteed due to the installment and cost limit of various sensors. Therefore, this paper proposes an enhanced 3D sensor deployment method to break through the sensing capabilities of CAVs’ own equipment limitations. By efficiently utilizing road infrastructure, reasonable roadside sensor deployment will effectively assist CAVs to expand the sensing range and improve overall sensing accuracy. Firstly, in order to address the limitations of existing works that often rely on simplified sensor models and idealized road conditions, we propose a Bresenham-based sensor and environment model that can be used to construct realistic road environments. Secondly, a decision transformer (DT)-based method is adopted to solve the problem of optimal deployment of sensors in road environments. Our approach effectively addresses the limitations of traditional static deployment methods, which often fail to consider the complexities of real-world driving conditions and the diverse factors influencing optimal sensor deployment. Finally, in order to solve the problem of DT delayed rewards, we propose a two-layer optimization method to redistribute the reward function to solve the challenge of local optimization. A large number of simulations oriented to sensing effects not only verify the effectiveness of the sensor deployment method but also ensure the reliability of sensing assistance.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it