Advancement in testing and verification methodologies is one of the key requirements for the commercialization and standardization of autonomous driving. Even though great progress has been made, the main challenges encountered during testing of autonomous vehicles, e.g., high number of test scenarios, huge parameter space and long simulation runs, still remain. In order to reduce current testing efforts, we propose an innovative method based on surrogate models in combination with stochastic optimization. The approach presents an iterative zooming-in algorithm aiming to minimize a given cost function and to identify faulty behavior regions within the parameter space. The surrogate model is updated in each iteration and is further used for intensive evaluation tasks, such as exploration and optimization.

In the development cycle of an Advanced Driver Assistance System (ADAS) emphasis is always placed on passenger safety, which directly correlates to adequate and extensive testing and validation procedures. However, testing and validation of ADAS systems is not a simple task and cannot be performed in a similar manner as conventional testing approaches. The main challenges is the vast amount of scenarios and environment parameter variations that might occur during operation. Current validation and testing procedures mostly rely on real world tests conducted on roads; however, because of the cost and complexity, these tests are not exhaustive and compromises on the scenario types and the number of considered parameters are made. In this paper we propose a model-based validation approach performed in a SiL (Soft-ware in the Loop) environment to validate an ADAS system under various conditions and the proposed methodology is presented on a Highway Pilot case study.

Test, verification, and development activities of vehicles with ADAS (Advanced Driver Assistance Systems) and ADF (Automated Driving Functions) generate large amounts of measurement data. To efficiently evaluate and use this data, a generic understanding and classification of the relevant driving scenarios is necessary. Currently, such understanding is obtained by using heuristic algorithms or even by manual inspection of sensor signals. In this paper, we apply deep learning on sensor time series data to automatically extract relevant features for classification of driving scenarios relevant for a Lane-Keep-Assist System. We compare the performance of convolutional and recurrent neural networks and propose two classification models. The first one is an online model for scenario classification during driving. The second one is an offline model for post-processing, providing higher accuracy.

Today, in our landscape perception exists a gap that needs to be fulfilled. It's important to increase the coverage, temporal and spatial resolution in order to cover this gap, as well as reduce costs with human resources that usually take this kind of tasks. Unmanned Autonomous vehicles with their inherent autonomy and reduced needs of human and communication resources, can provide additional capabilities and a new innovative solution to this problemThis paper presents and describes the participation of ICARUS Team at euRathlon 2015 and the importance of this type of events performed with multiple unnamed systems.

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors’ measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds’ alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments.