Imitation Learning for Vision-based Lane Keeping Assistance

This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is driving a vehicle. The policy is successfully learned via imitation learning using real-world data collected from human drivers and is evaluated in closed-loop simulated environments, demonstrating good driving behaviour and a robustness for domain changes. Evaluation is based on two proposed performance metrics measuring how well the vehicle is positioned in a lane and the smoothness of the driven trajectory.Comment: International Conference on Intelligent Transportation Systems (ITSC

On multi-objective optimization of planetary exploration rovers applied to ExoMars-type rovers

ExoMars is the first robotic mission of the Aurora program of the European Space Agency (EAS). Surface mobility (as provided by ExoMarks rover) is one of the enabling technologies necessary for future exploration missions. This work uses previouly developed mathematical models to represent an ExoMars rover operation in soft/rocky terrain. The models are used in an optimization loop to evaluate multiple objective functions affected by the change in geometrical design parameters. Several objective funktions can be used in our optimization environment powered by MOPS (Multi-Objective Parameter Synthesis). Two environments are used to simulate the rover in stability sensitive conditions and power and sinkage sensitive conditions. Finally, an ExoMars-like configuration is proposed and consistent improvemnt directions are pointed out

Self-consistent simulation of plasma scenarios for ITER using a combination of 1.5D transport codes and free-boundary equilibrium codes

Self-consistent transport simulation of ITER scenarios is a very important tool for the exploration of the operational space and for scenario optimisation. It also provides an assessment of the compatibility of developed scenarios (which include fast transient events) with machine constraints, in particular with the poloidal field (PF) coil system, heating and current drive (H&CD), fuelling and particle and energy exhaust systems. This paper discusses results of predictive modelling of all reference ITER scenarios and variants using two suite of linked transport and equilibrium codes. The first suite consisting of the 1.5D core/2D SOL code JINTRAC [1] and the free boundary equilibrium evolution code CREATE-NL [2,3], was mainly used to simulate the inductive D-T reference Scenario-2 with fusion gain Q=10 and its variants in H, D and He (including ITER scenarios with reduced current and toroidal field). The second suite of codes was used mainly for the modelling of hybrid and steady state ITER scenarios. It combines the 1.5D core transport code CRONOS [4] and the free boundary equilibrium evolution code DINA-CH [5].Comment: 23 pages, 18 figure

Multimodal 3D Object Detection from Simulated Pretraining

The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models. In order for these models to generalize to real-world applications, it is critical that the underlying dataset contains a variety of driving scenarios and that simulated sensor readings closely mimics real-world sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a novel tool for generating training data from the CARLA simulator to be used in autonomous driving research. The tool is able to export high-quality, synchronized LIDAR and camera data with object annotations, and offers configuration to accurately reflect a real-life sensor array. Furthermore, we use this tool to generate a dataset consisting of 10 000 samples and use this dataset in order to train the 3D object detection network AVOD-FPN, with finetuning on the KITTI dataset in order to evaluate the potential for effective pretraining. We also present two novel LIDAR feature map configurations in Bird's Eye View for use with AVOD-FPN that can be easily modified. These configurations are tested on the KITTI and CADET datasets in order to evaluate their performance as well as the usability of the simulated dataset for pretraining. Although insufficient to fully replace the use of real world data, and generally not able to exceed the performance of systems fully trained on real data, our results indicate that simulated data can considerably reduce the amount of training on real data required to achieve satisfactory levels of accuracy.Comment: 12 pages, part of proceedings for the NAIS 2019 symposiu