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Engine Mapping is usually performed under nominal conditions which include a humidity level of 8 g/Kg. Customers driving at different humidity conditions (which may range from 1 g/Kg in dry and colder climates and up to 35 g/Kg as in tropical climates) may experience a degraded performance due to the errors in engine torque estimation provided by the ECU. The torque estimation error interacts with many other features that affect drivability, such as the peak performance of the engine, transmission shift quality, etc. This paper extends the investigation in Part-1 by analyzing and quantifying the torque estimation error that may result in certain customer use cases at high humidity conditions, due to the mismatch between calibrated and actual conditions. The analysis is mainly performed for Speed-Density systems (MAP sensor based) but the effect of mass air flow sensor (MAF sensor) based systems is also briefly considered.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.