Towards probabilistic analysis of human-system integration in automated driving

According to the Automated Driving Roadmap ERTRAC 17, only vehicles of Level 5 may not need human interference. The current adaptive cruise control system or more advanced automated driving solutions below Level 5 require, therefore, that a human driver takes over, if an extraordinary situation occurs. A critical safety problem may be caused by the very short time span available to the driver. It has been recently demonstrated, mostly in application to the aerospace domain, how probabilistic analytical modeling (PAM) could effectively complement computational simulation techniques in various human-in-the-loop (HITL) related missions and off-normal situations, when the reliability of the equipment (instrumentation), both hard- and software, and the performance of the human contribute jointly to their likely outcome. Our objective is to extend this approach, with appropriate modifications, to safety analyses of automated driving applications

A probabilistic model of taking-over control from semi-autonomous vehicles

In automated driving scenarios with semi-autonomous vehicles of today and the near future, a request for taking-over control may at any time be communicated to a human driver. Empirical studies exist of, e.g., the effects of different modalities used in this human-machine interaction, and of drowsiness of drivers involved, depending on manual or automated driving. However, there is no model available yet of how the decision-making time in the course of taking-over control from the automated vehicle depends on the driver's drowsiness. Hence, we present a probabilistic model of the decision-making time as influenced by certain properties characterizing the driver’s drowsiness according to previous work

Towards Eliminating Overreacted Vehicular Maneuvers: Part II Comparative Analyses

\ua9 2019, Springer Nature Singapore Pte Ltd. Microscopic car following models are of great importance to traffic flow studies and vehicular dynamics reproducing. The Full Velocity Difference (FVD) model is a well-known example with satisfactory simulation performances in most times. However, by analyzing the structure of the model formula, we find that it can sometimes generate overreacted vehicular maneuvers such as unrealistically strong (overshooting for short) accelerations or decelerations that conflict with normal driver habits or even beyond the actual vehicular acceleration/deceleration performance, especially when the target vehicle encounter a leader cut-in or move-out (leader lane change for short). As Part II of the entire research, this paper conducts performance comparative analyses between the existing FVD model and the capped Full Velocity Difference (capped-FVD) model introduced in Part I of the research (the other companion paper) to address the above deficiency, and the results indicate that both models are equivalent in most times but the capped-FVD model will outperform the existing FVD model in aforementioned traffic scenarios since overreacted vehicular maneuvers (overshooting accelerations or decelerations) are totally eliminated. In other words, the aforementioned deficiency of the existing FVD model is totally corrected by the capped-FVD model and the capped-FVD model is a better choice for simulating vehicle movements in multi-lane roadways

Towards Eliminating Overreacted Vehicular Maneuvers: Part I Model Development and Calibration

Microscopic car following models are of great importance to traffic flow studies and vehicular dynamics reproducing. The Full Velocity Difference (FVD) model is a well-known example with satisfactory simulation performances in most times. However, by analyzing the structure of the model formulas, we find that it can sometimes generate overreacted vehicular maneuvers such as unrealistically strong (overshooting for short) accelerations or decelerations that conflict with normal driver habits or even beyond the actual vehicular acceleration/deceleration performance, especially when the target vehicle encounter a leader cut-in or move out (leader lane change for short). As Part I of the entire research, this paper corrects the above deficiency of the FVD model by proposing a capped-Full Velocity Difference (capped-FVD) model in which we limit any potential overshooting accelerations or decelerations generated to a reasonable range. Then, all model parameters are also calibrated using field data. Performance comparative analyses to validate the performance improvement of the capped-FVD model are included in the other companion paper serving as Part II of this research