Integrated Steering and Differential Braking for Emergency Collision Avoidance in Autonomous Vehicles
Bibliographic record
Abstract
Controlling the lateral dynamics of an autonomous vehicle confronting a sudden obstacle requires optimal use of tires' force capacities. In these situations, autonomous steering may not be able to respond fast enough to prevent collision or instability. This paper presents an integrated controller for autonomous vehicles, capable of suitably reacting to emergency situations when a sudden obstacle appears on the road. The proposed controller employs differential braking conservatively when needed, to produce an additional yaw moment, thereby improving a vehicle's lateral agility and responsiveness without endangering vehicle stability. A longitudinal controller is also designed to track a desired longitudinal velocity. Model predictive control (MPC) method is used for developing a combined path planning and tracking controller with a hierarchical structure that prioritizes (1) collision avoidance, (2) vehicle stability, and (3) path tracking. The effectiveness of the proposed integrated MPC controller is evaluated by simulating an experimentally validated CarSim model to demonstrate the controller's capability in preventing instability and collisions.
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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 | 0.000 |
| 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) | 0.001 | 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".