# A Machine Learning Model for Predicting Noise Limits of Motor Vehicles in UNECE R51 Regulations
## 1 Introduction  
Traffic noises, mainly generated by motor vehicle, aircraft, train, ship etc. during operation, refer to those unwanted sounds that hinder people's normal life and work. The motor vehicle noises are much severe when motor vehicles travel on main roads in municipal areas. These noises are unstable and random, and they greatly contribute sound pollutions to many audiences in urban regions. Their sound pressure levels keep changing with time and correlating with multiple factors, e.g., road type, vehicle type, vehicle number, and operating status of motor vehicles. The noise pollutions of motor vehicles are very large, and they can cause hearing reduction and damage of audiencesand generate significant impacts on audiences' emotion, rest and physical health, and interrupt their conversation and communication [1,2]. Therefore, it is vital to investigate the motor vehicle noises during operating and formulate and promulgate noise limits that fulfil the requirements regulated in automotive noise limits regulations. The automobile manufacturers are always pursuing to lower the noises emitted by their vehicles to advance the automobile technology and then improve the comfort of urban life, and continuously reducing adverse effects of noise on humans. Automobiles manufactured should follow the requirements according to local regulations and standards. These regulations and standards include International Organization for Standardization (ISO) standards for automobile noise regulations, American Society of Automotive Engineers (SAE) standards, Japanese regulations, United Nations Economic Commission for Europe (UNECE) regulations, etc. UNECE regulations are one of the most influential automotive technical regulations and certification systems in the world today [3]. In this study, the UNECE regulations were focused, which are based on the UN/ECE/WP29 \"1958 Agreement\" framework, and automobile products approved by the UNECE regulations are mutually recognised among parties to the \"1958 Agreement\". The current regulations of UNECE regulations concerning automobile noise are UNECE R51.03 \"Uniform Provisions Concerning the Approval of Motor Vehicles Having a Least Four Wheels with Regard to their Sound Emissions\", which came into effects on 16 October 1995, and the current revised version came into effects on 5 February 2016. It stipulates the measurement method on external noises of the vehicle when the vehicle accelerates, and external noises emitted by stationary road vehicles, respectively. It also specifies the limits of noise emission with the vehicle when it accelerates, as shown in Table 1.  
According to Table 1, the revisions of UNECE R51 regulations are becoming much frequently, from more than 10 years to recently 4 years as a cycle. It is challenging for the automotive industry to improve vehicle designs to fully fulfil the requirements. There were a certain number of researchers working on the investigation of automotive vehicle exterior noises. [PERSON] et al. [4] developed a hybrid model for the calculation of binaural vehicle exterior noise to simulate the situation of traffic noise in cities and suburbs, and the model used recordings of the main excitation signals (exhaust system, intake, engine, tires) and binaural filters describing the transfer paths from the sources to an artificial head standing at a distance of several meters outside the vehicle. [PERSON] [5] proposed a contact model to predict the tyre-pavement interaction noise for the pavement and the tyre, which was identified as main sources of vehicle exterior noise. In this work, a precise tyre model was created considering orthotropic material properties and described interactions between belt and sidewalls, inflation pressure and rotation of the tyre, etc. [PERSON] et al. [6] carried out a practical measurement on exterior noise of road vehicle in motion and evaluated exterior noise reductions at various levels of engine speed. [PERSON] et al.