Perception et anticipation du comportement d'autrui en situation simulée de conduite automobile
Bibliographic record
Abstract
PERCEPTION AND ANTICIPATION OF OTHERS’ BEHAVIOUR IN A SIMULATED CAR DRIVING SITUATION Anticipating the behaviour of others is a central mechanism in managing our interactions with other people, particularly in directing the development of the interaction. When the people concerned are in continual close physical proximity, the interactants can anticipate another person’s behaviour not only by means of implicit and explicit verbal clues, but also through behavioural clues (gestures, eye movement, posture, etc.). The importance of these clues in interpreting interactions has been highlighted in many studies that are largely inspired by ethnomethodology. In this paper we focus on an interaction situation that has the novelty of necessarily keeping the interactants at a distance. This forces them to manage a high level of interdependence with only reduced resources to communicate their intentions, their action objectives and their representation of the situation. The subject dealt with is car driving. A number of studies have examined the nature of interactions between drivers and their consequences for the overall driving system, particularly in the case of conflicts and accident situations. However, an analysis of the mechanisms brought into play to recognise the intentions of others has never been carried out, even though this is an indispensable component in anticipating the behaviour of drivers. This is the aim of our study. We analyse the way in which drivers infer the future actions of other drivers. We show that anticipating another’s actions involves the gathering of clues that are then built up on the basis of permanent representations (formal and informal rules, stereotypes, schemas) as well as circumstantial representations (situational clues and behavioural clues) that the drivers have in a particular situation. Our study indicates that these clues are far from being shared by all drivers even if, in most cases, they concur on the probable outcome of the interaction. These converging anticipations, carried out from particular representations, reveal that drivers have differing styles of prediction : whereas some show an environment predictive style, others adopt a behaviour predictive style. Finally, we show that in a familiar situation, and therefore one that is likely to evoke routine knowledge and subsymbolic processing, the prediction of the action is not based on any explicit clue. It is only when the situation presents particular characteristics which make it difficult to associate with a known situation that the subjects have to activate a symbolic processing of the situation, and are able to indicate more clues. In certain cases, therefore, difficulty in predicting the action may lead to indecision. In conclusion, we stress the increased need to integrate the perception of others in risk models applied to car driving. Each driver must have an appropriate representation of the interaction situation so that the road is really " readable " for the drivers. Thus, a more systematic point of view (Infrastructure-Driver-Other drivers) must be adopted to analyse driving situations. It is also important that each driver’s frame of reference should not differ too greatly from that of the other drivers. The training of future drivers or the introduction of new communication tools must therefore facilitate the construction and maintenance of a common frame of reference in interaction situations. This is all the more crucial when one considers that introducing new systems to assist driving tends to modify certain aspects of drivers’ behaviour, which then becomes more difficult to interpret by other drivers. Research into the anticipation mechanisms involved in interaction management should make it possible to anticipate and correct the situations that lead to a failure to read and interpret the situation. On a broader scale, our results contribute to a better understanding of jointly constructed situations such as free-flight.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".