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The major contribution of this paper is to propose a low-cost accurate distance estimation approach. It can potentially be used in driver modelling, accident avoidance and autonomous driving. Based on MATLAB and Python, sensory data from a Continental radar and a monocular dashcam were fused using a Kalman filter. Both sensors were mounted on a Volkswagen Sharan, performing repeated driving on a same route. The established system consists of three components, radar data processing, camera data processing and data fusion using Kalman filter. For radar data processing, raw radar measurements were directly collected from a data logger and analyzed using a Python program. Valid data were extracted and time stamped for further use. Meanwhile, a Nextbase monocular dashcam was used to record corresponding traffic scenarios. In order to measure headway distance from these videos, object depicting the leading vehicle was first located in each frame.

The turbo-compounding has been extensively researched as a mean of improving the overall thermal efficiency of the internal combustion engine. Many of the studies aiming to optimize the turbo-compounding system lead to the unified conclusion that this approach is more suitable for the operation under constant high load condition, while it has little effect on improving the fuel economy under low load conditions. Besides, in a traditional series turbo-compounding engine, the increased back pressure unavoidably results in a serious parasitic load to the system by increasing the resistance to the scavenging process. In order to improve this situation, a novel turbo-compounding arrangement has been proposed, in which the turbocharger was replaced by a continuously variable transmission (CVT) coupled supercharger (CVT superchargedr) to supply sufficient air mass flow rate to the engine at lower engine speeds.

With ever stricter legislative requirements for CO2 and other exhaust emissions, significant efforts by OEMs have launched a number of different technological strategies to meet these challenges such as Battery Electric Vehicles (BEVs). However, a multiple technology approach is needed to deliver a broad portfolio of products as battery costs and supply constraints are considerable concerns hindering mass uptake of BEVs. Therefore, further investment in Internal Combustion (IC) engine technologies to meet these targets are being considered, such as lean burn gasoline technologies alongside other high efficiency concepts such as dedicated hybrid engines. Hence, it becomes of sound reason to further embrace diversity and develop complementary technologies to assist in the transition to the next generation hybrid powertrain. One such approach is to provide increased valvetrain flexibility to afford new degrees of freedom in engine operating strategies.

The use of the chassis dynamometer test cells has been an integral part of the vehicle development and validation process for several decades, involving specialists from different fields, not all of them necessarily experts in automotive engineering. CHASSIS DYNAMOMETER TESTING: Addressing the Challenges of New Global Legislation (WLTP and RDE) sets out to gather knowledge from multiple groups of specialists to better understand the testing challenges associated with the vehicle chassis dynamometer test cells, and enable informed design and use of these facilities.