From Software-Defined Vehicles to Self-Driving Vehicles: A Report on CPSS-Based Parallel Driving

On June 11th, 2017, the 28th IEEE Intelligent Vehicles Symposium (IV'2017) was held in Redondo Beach, California, USA. As one of the 8 workshops at IV'2017, the cyber-physical-social systems (CPSS)-based parallel driving (WS'08), organized by the State Key Laboratory for Management and Control of Complex Systems (SKL-MCCS), Institute of Automation, Chinese Academy of Sciences, China, Xi'an Jiaotong University, China, Tsinghua University, China, Indiana University-Purdue University Indianapolis, USA, and Cranfield University, U.K, has attracted both researchers and practitioners in intelligent vehicles. About 60-70 participants from various countries had extensive and deep discussions on definition, challenges and alternative solutions for CPSS-based parallel driving, and widely agreed that it is a novel paradigm of cloud-based automated driving technologies. Six speakers shared their ideas, studies, field applications, and vision for future along these emerging directions from software-defined vehicles to self-driving vehicles

Game Theory For Self-Driving Cars

Pedestrian behaviour understanding is of utmost importance for autonomous vehicles (AVs). Pedestrian behaviour is complex and harder to model and predict than other road users such as drivers and cyclists. In this paper, we present an overview of our ongoing work on modelling AV-human interactions using game theory for autonomous vehicles control

Adaptive Perception, State Estimation, and Navigation Methods for Mobile Robots

In this cumulative habilitation, publications with focus on robotic perception, self-localization, tracking, navigation, and human-machine interfaces have been selected. While some of the publications present research on a PR2 household robot in the Robotics Learning Lab of the University of California Berkeley on vision and machine learning tasks, most of the publications present research results while working at the AutoNOMOS-Labs at Freie Universität Berlin, with focus on control, planning and object tracking for the autonomous vehicles "MadeInGermany" and "e-Instein"

Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving

Adverse weather conditions and occlusions in urban environments result in impaired perception. The uncertainties are handled in different modules of an automated vehicle, ranging from sensor level over situation prediction until motion planning. This paper focuses on motion planning given an uncertain environment model with occlusions. We present a method to remain collision free for the worst-case evolution of the given scene. We define criteria that measure the available margins to a collision while considering visibility and interactions, and consequently integrate conditions that apply these criteria into an optimization-based motion planner. We show the generality of our method by validating it in several distinct urban scenarios

Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

Most reinforcement learning approaches used in behavior generation utilize vectorial information as input. However, this requires the network to have a pre-defined input-size -- in semantic environments this means assuming the maximum number of vehicles. Additionally, this vectorial representation is not invariant to the order and number of vehicles. To mitigate the above-stated disadvantages, we propose combining graph neural networks with actor-critic reinforcement learning. As graph neural networks apply the same network to every vehicle and aggregate incoming edge information, they are invariant to the number and order of vehicles. This makes them ideal candidates to be used as networks in semantic environments -- environments consisting of objects lists. Graph neural networks exhibit some other advantages that make them favorable to be used in semantic environments. The relational information is explicitly given and does not have to be inferred. Moreover, graph neural networks propagate information through the network and can gather higher-degree information. We demonstrate our approach using a highway lane-change scenario and compare the performance of graph neural networks to conventional ones. We show that graph neural networks are capable of handling scenarios with a varying number and order of vehicles during training and application

Integrating tools for an effective testing of connected and automated vehicles technologies

The development of connected and automated driving functions involves that the interaction of autonomous/ automated vehicles with the surrounding environment will increase. Accordingly, there is a necessity for an improvement in the usage of traditional tools of the automotive development process. This is a critical problem since the classic development process used in the automotive field uses a very simplified driver model and the traffic environment, while nowadays it should contemplate a realistic representation of these elements. To overcome this issue, the authors proposed an integrated simulation environment, based on the co-simulation of Matlab/Simulink environment with simulation of urban mobility, which allows for a realistic model of vehicle dynamic, control logics, driver behaviour and traffic conditions. Simulation tests have been performed to prove the reasoning for such a tool, and to show the capabilities of the instrument. By using the proposed platform, vehicles may be modelled with a higher level of details (with respect to microscopic simulators), while the autonomous/automated driving functions can be tested in realistic traffic scenarios where the features of the road traffic environment can be varied to verify in a realistic way the level of robustness of the on-board implemented functions