Jointly Learnable Behavior and Trajectory Planning for Self-Driving\n Vehicles
Bibliographic record
Abstract
The motion planners used in self-driving vehicles need to generate\ntrajectories that are safe, comfortable, and obey the traffic rules. This is\nusually achieved by two modules: behavior planner, which handles high-level\ndecisions and produces a coarse trajectory, and trajectory planner that\ngenerates a smooth, feasible trajectory for the duration of the planning\nhorizon. These planners, however, are typically developed separately, and\nchanges in the behavior planner might affect the trajectory planner in\nunexpected ways. Furthermore, the final trajectory outputted by the trajectory\nplanner might differ significantly from the one generated by the behavior\nplanner, as they do not share the same objective. In this paper, we propose a\njointly learnable behavior and trajectory planner. Unlike most existing\nlearnable motion planners that address either only behavior planning, or use an\nuninterpretable neural network to represent the entire logic from sensors to\ndriving commands, our approach features an interpretable cost function on top\nof perception, prediction and vehicle dynamics, and a joint learning algorithm\nthat learns a shared cost function employed by our behavior and trajectory\ncomponents. Experiments on real-world self-driving data demonstrate that\njointly learned planner performs significantly better in terms of both\nsimilarity to human driving and other safety metrics, compared to baselines\nthat do not adopt joint behavior and trajectory learning.\n
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 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 | 0.001 | 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 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".