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Coating is a technique employed for the surface of materials to have thermal insulation, hot corrosion and oxidation resistance. Ion implantation forms modifications in surface composition or morphology of solids which yield to a change of physical and especially mechanical properties such as hardness and modulus of elasticity. The objective of this investigation is to concentrate on the friction and wear behaviour of TiN, N₂ and Zr implanted and TiN and Tinalox PVD coated 316L stainless steel and compare with a substrate. Mainly stainless steels were of attraction, because they frequently demonstrate a poor tribological behaviour, which can be enhanced when they are hardened by incorporating N₂, TiN Tinalox and Zr and forming a hardened surface zone.

The tribological properties such as surface hardness, friction and wear have been studied for AISI 316L stainless steel substrates which were co‐ion implanted with zirconium and oxygen ions. It is found that the wear resistance for AISI 316L stainless steel substrates implanted with zirconium and oxygen ions increased quite a lot. It is concluded that the increase in surface microhardness and the decrease in friction coefficient of AISI 316L stainless steel substrates play an important role in improving the wear resistance, and the relationship between relative wear volume and microhardness is correlated for zirconium and oxygen co‐ion implantation.

With the aid of naturalistic simulations, this paper aims to investigate human behavior during manual and autonomous driving modes in complex scenarios.

The simulation environment is established by integrating virtual reality interface with a micro-simulation model. In the simulation, the vehicle autonomy is developed by a framework that integrates artificial neural networks and genetic algorithms. Human-subject experiments are carried, and participants are asked to virtually sit in the developed autonomous vehicle (AV) that allows for both human driving and autopilot functions within a mixed traffic environment.

Not surprisingly, the inconsistency is identified between two driving modes, in which the AV’s driving maneuver causes the cognitive bias and makes participants feel unsafe. Even though only a shallow portion of the cases that the AV ended up with an accident during the testing stage, participants still frequently intervened during the AV operation. On a similar note, even though the statistical results reflect that the AV drives under perceived high-risk conditions, rarely an actual crash can happen. This suggests that the classic safety surrogate measurement, e.g. time-to-collision, may require adjustment for the mixed traffic flow.

Understanding the behavior of AVs and the behavioral difference between AVs and human drivers are important, where the developed platform is only the first effort to identify the critical scenarios where the AVs might fail to react.

This paper attempts to fill the existing research gap in preparing close-to-reality tools for AV experience and further understanding human behavior during high-level autonomous driving.

This work aims to systematically analyze the inconsistency in driving patterns between manual and autopilot modes in various driving scenarios (i.e. multiple scenes and various traffic conditions) to facilitate user acceptance of AV technology.

A close-to-reality tool for AV experience and AV-related behavioral study. A systematic analysis in relation to the inconsistency in driving patterns between manual and autonomous driving. A foundation for identifying the critical scenarios where the AVs might fail to react.