Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving
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
Recently, the concept of autonomous driving became prevalent in the domain of intelligent transportation due to the promises of increased safety, traffic efficiency, fuel economy and reduced travel time. Numerous studies have been conducted in this area to help newcomer vehicles plan their trajectory and velocity. However, most of these proposals only consider trajectory planning using conjunction with a limited data set (i.e., metropolis areas, highways, and residential areas) or assume fully connected and automated vehicle environment. Moreover, these approaches are not explainable and lack trust regarding the contributions of the participating vehicles. To tackle these problems, we design an Explainable Artificial Intelligence (XAI) Federated Deep Reinforcement Learning model to improve the effectiveness and trustworthiness of the trajectory decisions for newcomer Autonomous Vehicles (AVs). When a newcomer AV seeks help for trajectory planning, the edge server launches a federated learning process to train the trajectory and velocity prediction model in a distributed collaborative fashion among participating AVs. One essential challenge in this approach is AVs selection, i.e., how to select the appropriate AVs that should participate in the federated learning process. For this purpose, XAI is first used to compute the contribution of each feature contributed by each vehicle to the overall solution. This helps us compute the trust value for each AV in the model. Then, a trust-based deep reinforcement learning model is put forward to make the selection decisions. Experiments using a real-life dataset show that our solution achieves better performance than benchmark solutions (i.e., Deep Q-Network (DQN), and Random Selection (RS)).
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.016 | 0.061 |
| Research integrity | 0.000 | 0.001 |
| 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