An intrinsically motivated learning algorithm based on Bayesian surprise for cognitive radar in autonomous vehicles
Bibliographic record
Abstract
Introduction This paper proposes a Bayesian surprise learning algorithm that internally motivates the cognitive radar to estimate a target's state (i.e., velocity, distance) from noisy measurements and make decisions to reduce the estimation error gradually. The work exhibits how the sensor learns from experiences, anticipates future responses, and adjusts its waveform parameters to achieve informative measurements based on the Bayesian surprise. Methods For a simple vehicle-following scenario where the radar measurements are generated from linear Gaussian state-space models, the article adopts the Kalman filter to carry out state estimation. According to the information within the filter's estimate, the sensor intrinsically assigns a surprise-based reward value to the immediate past action and updates the value-to-go function. Through a series of hypothetical steps, the cognitive radar considers the impact of future transmissions for a prescribed set of waveforms–available from the sensor profile library–to improve the estimation process. Results and discussion Numerous experiments investigate the performance of the proposed design for various surprise-based reward expressions. The robustness of the proposed method is compared to the state-of-the-art for practical and risky driving situations. Results show that the reward functions inspired by estimation credibility measures outperform their competitors when one-step planning is considered. Simulation results also indicate that multiple-step planning does not necessarily lead to lower error, particularly when the environment changes abruptly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".