This paper evaluates the effect of the electrification of the small, medium, and large internal combustion engine (ICE) passenger cars on the levels of total particulate matter (PM). The total mean PM₁₀ and PM_2.5 emission factors (EFs) on urban, rural, and motorway roads are in the range of 26.13 - 39.57 mg km^-1 veh^-1 and 13.39 - 18.44 mg km^-1 veh^-1, respectively, from small to large ICE passenger cars. Correspondingly, the total mean PM₁₀ and PM_2.5 non-exhaust EFs on urban, rural, and motorway roads range from 27.76 to 43.43 mg km^-1 veh^-1 and 13.17 -19.24 mg km^-1 veh^-1 from equivalent small to large electric vehicles (EVs) without regenerative braking. These results show that the total non-exhaust PM from the equivalent EVs may exceed all PM from ICE passenger cars, including exhaust particle emissions, which are dependent mainly on the extent of regenerative braking, followed by passenger car type and road type. PM₁₀ EFs for equivalent EVs without regenerative braking on urban, rural, and motorway roads are all higher than those from ICE cars. As for PM_2.5, most of the equivalent EVs require different extents of regenerative braking to reduce brake emissions to be in line with all particle emissions from relative ICE cars.

Tyre wear generates not only large pieces of microplastics but also airborne particle emissions, which have attracted considerable attention due to their adverse impacts on the environment, human health, and the water system. However, the study on tyre wear is scarce in real-world driving conditions. In the present study, the left-front and left-rear tyre wear in terms of volume lost in mm³ of 76 taxi cars was measured about every three months. This study covered 22 months from September 2019 to June 2021 and included more than 500 measurements in total. Some of the data was used to evaluate the effects of vehicle type and tyre type on tyre wear. In addition, a machine learning method (i.e., Extreme gradient boosting (XGBoost)) was used to probe the effect of driving behaviour on tyre wear by monitoring real-time driving behaviour. The current statistical results showed that, on average, the tyre wear was 72 mg veh^-1 km^-1 for a hybrid car and 53 mg veh^-1 km^-1 for a conventional internal combustion engine car. The average tyre wear measured for a taxi vehicle configuration featuring winter tyres was 160 mg veh^-1 km^-1, which was 1.4 and 3.0 times as much as those with all-season tyres and summer tyres, respectively. The wear rate of left-front tyres was 1.7 times higher than that of left-rear tyres. The XGBoost results indicated that compared to driving behaviour, tyre type and tyre position had more important effects on tyre wear. Among driving behaviours, braking and accelerating events presented the most considerable impact on tyre wear, followed by cornering manoeuvres and driving speed. Thus, it seems that limiting harsh braking and acceleration has the potential to reduce tyre wear significantly.

Some of the first ‘automated’ vehicles to be deployed on our roads will require a system of shared driving with a human driver. While this creates technical and operational challenges, the law must also facilitate such a transfer. One method may be to obtain the driver’s consent to share operational responsibility and to delineate legal responsibility between vehicle and driver in the event of an accident. Consent is a voluntary agreement where an individual is aware of the potential consequences of their consent, including the risks. The driver of a partially automated vehicle must be informed of potential risks before giving consent to share operational responsibility. This paper will refer to the inherent dangers associated with shared operational responsibility, in particular where there has been a request for the driver to take back control from the automated vehicle during the journey. Drivers are likely to experience delay in regaining situational awareness, making such operational transfers hazardous. It is argued that where an interactive digital interface is used to convey information, such as driver responsibility, risk and legal terms, drivers may fail to sufficiently process such communications due to fundamental weaknesses in human–machine interaction. The use of an interactive digital interface alone may be inadequate to effectively communicate information to drivers. If the problems identified are not addressed, it is argued that driver consent may be inconsequential, and fail to facilitate a predicable demarcation of legal responsibility between automated vehicles and drivers. Ongoing research into automated vehicle driver training is considered as part of the preparation required to design driver education to a level whereby drivers may be able to sufficiently understand the responsibilities involved in operating a partially automated vehicle, which has implications for future driver training, licensing and certification.