Simulation Framework for Vehicle and Electric Scooter Interaction

TL;DR

This work tackles safety analysis for vehicle–e-scooter interactions in urban traffic by introducing a model-based VEI simulation framework. It combines system design (ODD) and simulation establishment with e-scooter and vehicle motion models, a perception module, and a hybrid social force–finite-state machine approach for the e-scooter. Three VEI use cases are studied through qualitative and quantitative analyses to assess risk under different aggressiveness levels and traffic configurations. The framework demonstrates the ability to quantify risk factors and serves as a practical tool for traffic safety evaluation and future integration with connected and automated vehicle systems.

Abstract

The number of shared micro-mobility services such as electric scooters (e-scooters) has an increasing trend due to the advantages of high efficiency and low cost in short-range travel in urban areas. However, due to the unique characteristics of moving behavior, it is commonly seen that e-scooters may share the road with other motor vehicles. The lack of protection may lead to severe injury for e-scooter riders. The scenario where an e-scooter crosses an intersection or makes a lane change while interacting with an approaching vehicle was commonly seen in real-life traffic data. Such scenarios are hazardous because the intention and behavior of the e-scooter may vary significantly based on the traffic environment conditions. Furthermore, some other vehicles may occlude the presence of the moving e-scooter, which can result in an unexpected collision. In this paper, we propose a simulation platform to mimic the interactions between vehicles and e-scooters. Several traffic scenarios are studied via qualitative and quantitative analysis. The proposed framework is shown to be valuable and efficient for the general risk analysis for vehicle and e-scooter interactions (VEI).

Figures (7)

• Figure 1: Two risky traffic scenarios where the e-scooter wants to cross the intersection with a crossing vehicle is approaching (a) or makes a lane change when a vehicle is approaching from behind (b).
• Figure 2: Framework for the proposed model-based traffic simulation.
• Figure 3: State transition of the motion planner for the e-scooter.
• Figure 4: Illustration of the vehicle e-scooter interaction scenario in an intersection with two crossing vehicles. The red dot shows the position of the e-scooter. The red sector represents the FOV of the e-scooter. The blue star is the destination that the e-scooter targets to reach. The yellow boxes represent individual vehicles.
• Figure 5: Screenshots of the simulation in the intersection scenario with Aggressive and Normal types of e-scooters. The interaction process of an Aggressive e-scooter is shown in the first row of the graph. The e-scooter first approached the waiting position and started to cross the interaction while the moving vehicle began crossing the intersection at a much higher speed. In the end, the collision occurred at $t=7.6s$. The second row of the graph shows the interaction process of a Normal e-scooter. Due to the higher crossing threshold, the e-scooter kept waiting at the decision-making position. The e-scooter kept a safe distance from the crossing vehicle and avoided a potential collision.