Department of Informatics, University of Minho, 4710-057 Braga, Portugal.
Department of Information Systems, University of Minho, 4804-533 Guimarães, Portugal.
Sensors (Basel). 2023 Jan 22;23(3):1260. doi: 10.3390/s23031260.
Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must be achieved by the drivers.
智能交通系统（ITSs）旨在为道路使用者提供创新服务，以提高交通效率、机动性和安全性。对于弱势道路使用者（VRUs）来说，这一方面的安全至关重要，因为这些使用者通常更容易处于危险境地，而且与道路上的常规车辆相比，他们的车辆安全机制也较差。为 VRU 车辆实施自动安全解决方案具有挑战性，因为它们的灵活性很高，并且难以预测它们的行为。然而，如果配备了通信能力，则可以利用车辆到任何事物（V2X）数据来实施此类自动系统。这项工作提出了一种 VRU（摩托车手）碰撞预测系统，该系统利用在通信数据上叠加的堆叠单向长短期记忆网络（LSTM），这些通信数据是使用 VEINS 仿真框架生成的（结合了 Simulation of Urban MObility（SUMO）和 Network Simulator 3（ns-3）工具）。所提出的系统在两个不同场景中表现良好：在场景 A 中，它预测了 96%的碰撞，平均平均预测时间（s）（APT）为 4.53 s，正确决策百分比（CDP）为 41%，假阳性（FP）为 78；在场景 B 中，它预测了 95%的碰撞，APT 为 4.44 s，而 CDP 为 43%，假阳性为 68。结果表明了该方法的有效性：在 V2X 数据上使用 ML 方法允许预测大多数模拟事故。尽管如此，存在相对较高数量的 FP 不允许使用安全功能（例如，乘用车紧急制动）；因此，必须由驾驶员实现碰撞避免。
