A Configurable Simulation Framework for Safety Assessment of Vulnerable Road Users

TL;DR

The paper tackles VRU safety in automated and connected driving by delivering a lightweight, configurable simulation framework aligned with Euro NCAP VRU tests. It combines a rule-based finite-state machine motion planner with optional V2X awareness to study safety margins across pedestrian, e-scooter, and motorcyclist scenarios, using a sustainability metric that balances safety, comfort, and efficiency. Key contributions include digitizing standardized VRU tests, implementing a baseline CAP controller, and demonstrating qualitative benefits of V2X-augmented planning for early braking and collision avoidance. This framework enables rapid prototyping and repeatable validation of VRU safety strategies, supporting broader case studies and infrastructure integration toward VRU-friendly ITS applications. In mathematical terms, safety distance follows the two-second rule, $D_{ m safe}=2 \, \cdot \, V_{ m veh}$, and collision avoidance decisions are driven by timely perception and VRU awareness within a defined field of view.

Abstract

Ensuring the safety of vulnerable road users (VRUs), including pedestrians, cyclists, electric scooter riders, and motorcyclists, remains a major challenge for advanced driver assistance systems (ADAS) and connected and automated vehicles (CAV) technologies. Real-world VRU tests are expensive and sometimes cannot capture or repeat rare and hazardous events. In this paper, we present a lightweight, configurable simulation framework that follows European New Car Assessment Program (Euro NCAP) VRU testing protocols. A rule-based finite-state machine (FSM) is developed as a motion planner to provide vehicle automation during the VRU interaction. We also integrate ego-vehicle perception and idealized Vehicle-to-Everything (V2X) awareness to demonstrate safety margins in different scenarios. This work provides an extensible platform for rapid and repeatable VRU safety validation, paving the way for broader case-study deployment in diverse, user-defined settings, which will be essential for building a more VRU-friendly and sustainable intelligent transportation system.

Figures (6)

• Figure 1: Three VRU testing scenarios based on the Euro NCAP VRU testing protocol euroncap: (a) Car-to-Pedestrian Nearside Adult, (b) Car-to-Bicyclist Longitudinal Adult, (c) Car-to-Motorcyclist Front Turn across Path. Apart from the testing benchmark, a roadside infrastructure can provide additional critical VRU information by sending vehicle VRU awareness messages. Each category of VRU has different moving behaviors, e.g., varying moving directions and speeds.
• Figure 2: Proposed VRU analysis framework based on standard testing protocol and model-based traffic simulation.
• Figure 3: State transition of the collision avoidance planner integrated with the perception module.
• Figure 4: Snapshots of the orthogonal pedestrian-crossing scenario. Top row (a)-(c): baseline CAP without V2X. (a) When $t=1.4\,\mathrm{s}$ pedestrian is still outside the ego vehicle’s FOV; (b) $t=3.3\,\mathrm{s}$: pedestrian enters the FOV and the ego vehicle begins braking, but the remaining gap is small; (c) At $t=5.0\,\mathrm{s}$ the vehicle collides with the pedestrian. Bottom row (d)-(f): CAP augmented with V2X. (d) Ego vehicle approaches the pedestrian with the same initial velocity at $t=1.4\,\mathrm{s}$; (e) Proactive deceleration starts before detection by the onboard sensor at $t=2.1\,\mathrm{s}$ when the ego vehicle enters the VRU awareness reception zone; (f) $t=5.8\,\mathrm{s}$: pedestrian clears the path and the collision is avoided.
• Figure 5: Snapshots of the same-lane e-scooter following scenario. Top row (a)-(c): CAP without V2X. The ego vehicle closes on a slower e-scooter, detects it at $t=3.0\,\mathrm{s}$, and reduces speed; by $t=4.8\,\mathrm{s}$ a safe headway is established. Bottom row (d)-(f) demonstrates the case when V2X is enabled to assist CAP. Early warning triggers a less gentle but more conservative deceleration profile. Both CAPs avoid collision successfully.