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The connectivity between vehicles, infrastructure, and other traffic participants brings a new dimension to automotive safety applications. Soon all the newly produced cars will have Vehicle to Everything (V2X) communication modems alongside the existing Advanced Driver Assistant Systems (ADAS). It is essential to identify the different sensor measurements for the same targets (Data Association) to use connectivity reliably as a safety feature alongside the standard ADAS functionality. Considering the camera is the most common sensor available for ADAS systems, in this paper, we present an experimental implementation of a Mahalanobis distance-based data association algorithm between the camera and the Vehicle to Vehicle (V2V) communication sensors. The implemented algorithm has low computational complexity and the capability of running in real-time. One can use the presented algorithm for sensor fusion algorithms or higher-level decision-making applications in ADAS modules.

Engine Mapping is usually performed under nominal conditions which include a humidity level of 8 g/Kg. Customers driving at different conditions (which may range from 1 g/Kg in colder and dry climates and up to 35 g/Kg as in tropical climates) may experience less-than-optimal engine combustion which results in reduced onroad fuel economy. Humidity has an EGR-equivalent effect, and measuring it will correct the spark timing, mainly at Maximum Brake Torque (MBT) and borderline conditions, and claim back some of those losses. This paper aims at quantifying the small fuel economy benefits associated with on-board humidity measurement for certain customer use cases at high humidity conditions. Dyno data was collected for a Ford 2.3L GTDI engine at three speed load points, and intake air humidity was varied between 20% and 80% relative humidity. The effect of humidity compensation on spark timing, combustion phasing, knock, and consequently on overall engine efficiency was analyzed.

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.

This paper presents an evaluation of two different Vehicle to Infrastructure (V2I) applications, namely Red Light Violation Warning (RLVW) and Green Light Optimized Speed Advisory (GLOSA). The evaluation method is to first develop and use Hardware-in-the-Loop (HIL) simulator testing, followed by extension of the HIL testing to road testing using an experimental connected vehicle. The HIL simulator used in the testing is a state-of-the-art simulator that consists of the same hardware like the road side unit and traffic cabinet as is used in real intersections and allows testing of numerous different traffic and intersection geometry and timing scenarios realistically. First, the RLVW V2I algorithm is tested in the HIL simulator and then implemented in an On-Board-Unit (OBU) in our experimental vehicle and tested at real world intersections.

Vulnerable Road User (VRU) safety has been an important issue throughout the years as corresponding fatality numbers in traffic have been increasing each year. With the developments in connected vehicle technology, there are new and easier ways of implementing Vehicle to Everything (V2X) communication which can be utilized to provide safety and early warning benefits for VRUs. Mobile phones are one important point of interest with their sensors being increased in quantity and quality and improved in terms of accuracy. Bluetooth and extended Bluetooth technology in mobile phones has enhanced support to carry larger chunks of information to longer distances. The work we discuss in this paper is related to a mobile application that utilizes the mobile phone sensors and Bluetooth communication to implement Personal Safety Message (PSM) broadcast using the SAE J2735 standard to create a Pedestrian to Vehicle (P2V) based safety warning structure.