Integration of Motion Prediction with End-to-end Latent RL for Self-Driving Vehicles
Bibliographic record
Abstract
The field of self-driving vehicles (SDVs) is going viral among researchers from a broad spectrum of specialties. SDVs are expected to have profound impacts on the world once fully developed and deployed on roads. Hence, researchers are working assiduously together to accomplish this project. In this paper, we propose integrating motion prediction with sequential latent maximum entropy reinforcement learning, end-to-end, to train an agent to navigate autonomously in a simulated urban environment. The fusion of motion prediction for surrounding vehicles enhances traffic efficiency and safety. A novel network specialized in joint perception and motion prediction, named MotionNet, is selected in our paper to supply us with motion predictions. Our proposed system demonstrates that adding motion prediction enhances performance even further. Furthermore, our system relies merely on LIDAR sensor. CARLA simulator is used to conduct our experiments and extract outcomes.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".