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Advanced Driver Assistance Systems and Automated Driving are a megatrend in the automotive industry. The following questions arise: Will vehicle manufacturers still be able to differentiate themselves “brand-specifically” in the future or will all vehicles be perceived the same when being driven? How can a brand DNA be implemented and how can the transfer of “fun to drive” to “fun to be driven” be achieved? In order to reach this, clear driving characteristic goals – in front of the customer – should be defined and the requirements for vehicle systems and components shall be derived from this. However, what are driving characteristics in the context of assisted and automated driving, Figure 1, and how can those specifically be achieved in the development? Porsche has addressed this question together with the University of Applied Sciences Kempten and MdynamiX. How can an attribute-based development look like and how can Porsche effectively design a brand-typical characteristic in this area?
How Can We Improve the Driving Experience with Human-Machine-Interface for Automated Driving?
(2020)
The head-up-display (HUD), which reflects driving information into the windshield has the goal to lower driving effort from the information uptake and thereby, increase our safety by reducing risks associated to e.g., fatigue and stress. This motivated us to test the HUD in combination with the lane keeping assistant system (LKAS) from n = 48 subjects who drove in real traffic conditions two premium vehicles in a highway in Germany. Subjects then rated the Human-Machine Interaction (HMI) from an assessment about the perceived feelings of safety, degree of relief, information displayed, displays design, and monitoring procedures. Results from CMP regressions show that the HUD has a significant effect on the driving effort and safety feelings, and on the overall subjects’ driving experience. Moreover, we find that this effect is stronger among elderly drivers, students, and females who feel significantly less driving effort. In particular, women felt significantly safer while the HUD was activated.
Deep learning for lateral vehicle control – an end-to-end trained multi-fusion steering model
(2019)
Deep Learning based behavior reflex methods found their way into modern vehicles. To model the human driving behavior it is not sufficient to rely solely on individual, noncontiguous camera frames without taking vehicle signals or road specific features into account. In this work four temporal fusion methods are evaluated based on three different Deep Learning models. The proposed spatio-temporal Mixed Fusion model extends the present end-to-end models and consist of multiple levels of fusions. The raw image data from a single front facing camera is mixed with recorded vehicle data and a map based predicted road bank angle gradient vector. The model accesses multiple time axes: temporal features of multiple image frames are extracted through a combination of Convolution and LSTM layers while it can also make assumptions about the future road condition with the use of upcoming Ground Truth road bank angle changes. Experiments are performed on a recorded data set of real world drivings. Results show, that this approach leads to an accurate imitation of the human driver with an inference capability of more than 60 FPS.
Advanced driver assistance systems (ADAS) support the driver in certain traffic situations and can increase road safety. For this appropriate interaction, concepts between the driver and the assistance systems are required, which focuses on driver’s needs. In a user-centered study with N = 48 subjects, interviews and questionnaires were conducted during a test drive in real road traffic in order to test and evaluate the lane keeping assistant system (LKAS) with head-up display (HUD). In addition, two current premium vehicles from various manufacturers were used to investigate the influence of the HUD on the user experience and to derive optimization potential for current and future automatic driving functions. In comparison to the test rides with LKAS in combination without HUD (with head-down display), it can be determined that there is a positive influence of HUD on the experience with LKAS.
Advanced Driver Assistance Systems (ADAS) and Highly Automated Driving Systems (HAD) are among the most important megatrends in the automotive development. Accompanying this one big question arises: do all assisted and automated driving cars drive the same or will vehicle manufacturers be able to differentiate themselves with DNA of their own? And - especially for a sportscar-manufacturer like Porsche – how can ADAS and HAD impart typical attributes like driving fun and sportiness, Figure 1? In order to achieve this, clear driving characteristic goals (from a customer’s point of view) must be defined and the system requirements for ADAS and HAD (including all components like sensors, ECU’s and actors) shall be derived from this. However, what are driving and brand characteristics in the context of assisted and automated driving? And how can those be realized in the development? Porsche has addressed this question together with the University of Applied Sciences Kempten and MdynamiX.
From an engineering point of view, church bells are structures that, during ringing, are exposed to severe loading conditions. They are damaged due to material wear, fatigue loading, material deficiencies, different clapper-to-bell layouts, ringing conditions, etc. To get an insight into the wear-related damage of bells, experimental investigations and numerical simulations of the local contact between the clapper and the bell were carried out as part of the activities of an EU-funded project called Maintenance and Protection of Bells. In order to make a full-scale comparison between the measured and simulated results a simplified model was set up. In this model the clapper was replaced by a cylinder with a rounded tip that was dropped against a block representing the bell wall. The aim of the simplified model was to study the impact phenomenon in a controlled way and to adapt the numerical model for simulating the local contact. In the article the synthesis of a finite-element model for simulating the cylinder-drop test is presented. The results of the finite-element simulations of repetitive cylinder drops are compared to the data that were measured in the laboratory. The effects of the cylinder material, the cylinder radius and the drop height of the cylinder on the local elastic–plastic behaviour of the cylinder and the block are also presented and discussed.