Modeling Human-Like Interaction Between Cyclists and Vehicles

Thu, 26 Oct 2023 00:00:00 +0000

Introduction

The study, using real-world data, aims to model the interaction between crossing bicycles and right-turning vehicles, attempting to alter the simulator’s guidance at the tactical level. In ten days of video recordings from an urban intersection, 517 valid cases were collected.

Figure 1. Trajectories of crossing bicycles and right-turning vehicles.

Modeling

A logistic regression model of crossing order was employed with features of bicycles (0) and vehicles (1):

speed (v)
speed difference (dv)
distance to crossing point (d2x)
predicted PET (ppet).

The dataset was split into training (80%) and testing (20%) subsets. Models with different feature combinations were cross-validated.

$$ \log\left(\frac{p}{1-p}\right) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \ldots + \beta_k x_k $$

Simulation

The selected model was implemented in SUMO: At 20 m before the crossing point, the crossing order was estimated, and the lagging object was forced to slow down. A comparison was performed between real data, SUMO’s default model, and this new model.

In reality, 74% of bicycles crossed before vehicles.
In the default model, vehicles were more conservative - bicycles crossed first in about 90% of cases.
The new model brings this ratio closer to 50%, though still not identical to real data.
In the default model, PET distributions vary strongly by crossing order, whereas in the real-world data and new model, PET distributions are similar (yet differ by ~1 s).

Conclusion

The study of interactions between bicycles and vehicles in real traffic contributes to improving simulator realism.
Although the new model did not perfectly reproduce the crossing-order distribution, it significantly improved the PET distribution, bringing it closer to reality.

Regression | Meng's Page

Modeling Human-Like Interaction Between Cyclists and Vehicles

Introduction

Modeling

Simulation

Conclusion