Search Results

Modern powertrain development is targeting to meet challenging, to some degrees contradictory development goals in a short timeframe. Looking to a development time schedule of 36 months from concept to SOP, it becomes a prerequisite that unnecessary design loops have to be avoided by all means. Now, in addition, the experimental development work has to be conducted more efficiently than in the past. In recent years methods for an efficient design process have been successfully applied. Testing and vehicle application work can take advantage of methods empowered by model based approaches. Today, models with different levels of detail are able to significantly improve nearly every development phase. Supported by standardized and automated test bench and vehicle procedures an efficient and comprehensive development process can be established and utilized, which is also necessary to tackle growing complexity.

This paper presents a cost effective development platform, named FEV-Driver, for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD). The FEV-Driver platform is an electric go-kart that was converted into an x-by-wire vehicle which represents the behavior of a full-scale electric vehicle. FEV-Driver has the advantage of being a small-scale vehicle that can be used with a significant lower safety risk compared to full-sized vehicles. The ADAS/AD algorithms for this platform were developed in both Simulink and C++ software and implemented within the Robot Operating System (ROS) middleware. Besides the description of the platform, Lane Keep Assist (LKA) and Automatic Emergency Braking (AEB) algorithms are discussed, followed by a path planning algorithm which enables the vehicle to drive autonomously after a manually controlled training lap. The modular system architecture allows for complete controller exchange or adaptation to different vehicles.