Automatic Generation of Road Geometries to Create Challenging Scenarios for Automated Vehicles Based on the Sensor Setup

For the offline safety assessment of automated vehicles, the most challenging and critical scenarios must be identified efficiently. Therefore, we present a new approach to define challenging scenarios based on a sensor setup model of the ego-vehicle. First, a static optimal approaching path of a road user to the ego-vehicle is calculated using an A* algorithm. We consider a poor perception of the road user by the automated vehicle as optimal, because we want to define scenarios that are as critical as possible. The path is then transferred to a dynamic scenario, where the trajectory of the road user and the road layout are determined. The result is an optimal road geometry, so that the ego-vehicle can perceive an approaching object as poorly as possible. The focus of our work is on the highway as the Operational Design Domain (ODD).Comment: Accepted at the 2020 IEEE Intelligent Vehicles Symposium (IV), October 20-23, 202

Systematic Analysis of the Sensor Coverage of Automated Vehicles Using Phenomenological Sensor Models

The objective of this paper is to propose a systematic analysis of the sensor coverage of automated vehicles. Due to an unlimited number of possible traffic situations, a selection of scenarios to be tested must be applied in the safety assessment of automated vehicles. This paper describes how phenomenological sensor models can be used to identify system-specific relevant scenarios. In automated driving, the following sensors are predominantly used: camera, ultrasonic, \radar and \lidarohne. Based on the literature, phenomenological models have been developed for the four sensor types, which take into account phenomena such as environmental influences, sensor properties and the type of object to be detected. These phenomenological models have a significantly higher reliability than simple ideal sensor models and require lower computing costs than realistic physical sensor models, which represents an optimal compromise for systematic investigations of sensor coverage. The simulations showed significant differences between different system configurations and thus support the system-specific selection of relevant scenarios for the safety assessment of automated vehicles.Comment: Published at 2019 IEEE Intelligent Vehicles Symposium (IV19), June 201

Identification of Challenging Highway-Scenarios for the Safety Validation of Automated Vehicles Based on Real Driving Data

For a successful market launch of automated vehicles (AVs), proof of their safety is essential. Due to the open parameter space, an infinite number of traffic situations can occur, which makes the proof of safety an unsolved problem. With the so-called scenario-based approach, all relevant test scenarios must be identified. This paper introduces an approach that finds particularly challenging scenarios from real driving data (\RDDwo) and assesses their difficulty using a novel metric. Starting from the highD data, scenarios are extracted using a hierarchical clustering approach and then assigned to one of nine pre-defined functional scenarios using rule-based classification. The special feature of the subsequent evaluation of the concrete scenarios is that it is independent of the performance of the test vehicle and therefore valid for all AVs. Previous evaluation metrics are often based on the criticality of the scenario, which is, however, dependent on the behavior of the test vehicle and is therefore only conditionally suitable for finding "good" test cases in advance. The results show that with this new approach a reduced number of particularly challenging test scenarios can be derived.Comment: Accepted at 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER

Towards Certification of Autonomous Driving: Systematic Test Case Generation for a Comprehensive but Economically-Feasible Assessment of Lane Keeping Assist Algorithms

utomation of the driving task continues to progress rapidly. In addition to improving the algorithms, proof of their safety is still an unsolved problem. For an automated driving function that does not require permanent monitoring by the driver, a theoretically infinite number of possible traffic situations must be tested. One promising method to overcome this problem is the scenario-based approach. This approach shall enable an economic certification of automated driving functions with sufficient test space coverage. However, even with this approach, the selection of the scenarios to be tested is still problematic. The first step is to consider a driver assistance system in order to reduce complexity. For the Lane Keeping Assist System under consideration, this paper defines a methodology as well as the scenarios for a comprehensive yet economically-feasible certification. Economical-feasibility of the presented methodology is shown in the results by an approximation of the resulting simulation costs for executing the defined test cases

A Business Model Canvas for iDecide - How to Design a New Decision Making App?

International audienceAs products become increasingly complex, product developers have to make decisions effectively and efficiently. Therefore, the long term goal of the SIG DM is to develop an iDecide App. This app should support developers in complex decision making situations during the development process. The aim of this paper is to describe possible business models for an iDecide App with the Business Model Canvas. Some business models and one industrial case study for an iDecide App are described. This should initiate an in-depth discussion on a detailed business model for an iDecide App