Cooperative Vehicle Perception and Localization Using Infrastructure-based Sensor Nodes
Bibliographic record
Abstract
Reliable and accurate Perception and Localization (PL) are necessary for safe intelligent transportation \nsystems. The current vehicle-based PL techniques in autonomous vehicles are vulnerable to occlusion \nand cluttering, especially in busy urban driving causing safety concerns. In order to avoid such safety \nissues, researchers study infrastructure-based PL techniques to augment vehicle sensory systems. \nInfrastructure-based PL methods rely on sensor nodes that each could include camera(s), Lidar(s), \nradar(s), and computation and communication units for processing and transmitting the data. Vehicle \nto Infrastructure (V2I) communication is used to access the sensor node processed data to be fused with \nthe onboard sensor data. \nIn infrastructure-based PL, signal-based techniques- in which sensors like Lidar are used- can provide \naccurate positioning information while vision-based techniques can be used for classification. \nTherefore, in order to take advantage of both approaches, cameras are cooperatively used with Lidar in \nthe infrastructure sensor node (ISN) in this thesis. ISNs have a wider field of view (FOV) and are less \nlikely to suffer from occlusion. Besides, they can provide more accurate measurements since they are \nfixed at a known location. As such, the fusion of both onboard and ISN data has the potential to improve \nthe overall PL accuracy and reliability. \nThis thesis presents a framework for cooperative PL in autonomous vehicles (AVs) by fusing ISN \ndata with onboard sensor data. The ISN includes cameras and Lidar sensors, and the proposed camera Lidar fusion method combines the sensor node information with vehicle motion models and kinematic \nconstraints to improve the performance of PL. One of the main goals of this thesis is to develop a wind induced motion compensation module to address the problem of time-varying extrinsic parameters of \nthe ISNs. The proposed module compensates for the effect of the motion of ISN posts due to wind or \nother external disturbances. To address this issue, an unknown input observer is developed that uses \nthe motion model of the light post as well as the sensor data. \nThe outputs of the ISN, the positions of all objects in the FOV, are then broadcast so that autonomous \nvehicles can access the information via V2I connectivity to fuse with their onboard sensory data through \nthe proposed cooperative PL framework. In the developed framework, a KCF is implemented as a \ndistributed fusion method to fuse ISN data with onboard data. The introduced cooperative PL \nincorporates the range-dependent accuracy of the ISN measurements into fusion to improve the overall \nPL accuracy and reliability in different scenarios. The results show that using ISN data in addition to onboard sensor data improves the performance and reliability of PL in different scenarios, specifically \nin occlusion cases.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".