Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Spec...

Human motion trajectory prediction: a survey:

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.

Autonomous vehicle perception: The technology of today and tomorrow

Over the past decade, vision-based vehicle detection techniques for road safety improvement have gained an increasing amount of attention. Unfortunately, the techniques suffer from robustness due to huge variability in vehicle shape (particularly for motorcycles), cluttered environment, various illumination conditions, and driving behavior. In this paper, we provide a comprehensive survey in a systematic approach about the state-of-the-art on-road vision-based vehicle detection and tracking systems for collision avoidance systems (CASs). This paper is structured based on a vehicle detection processes starting from sensor selection to vehicle detection and tracking. Techniques in each process/step are reviewed and analyzed individually. Two main contributions in this paper are the following: survey on motorcycle detection techniques and the sensor comparison in terms of cost and range parameters. Finally, the survey provides an optimal choice with a low cost and reliable CAS design in vehicle industries.

Vehicle Detection Techniques for Collision Avoidance Systems: A Review

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.

Human Motion Trajectory Prediction: A Survey

In this work, we propose a novel taxonomy to partition the problem of testing advanced driver assistance systems (ADAS) into three basic dimensions. These dimensions are detailed and confirmed with recent research. Our framework permits the consideration of open research questions which have to be answered to pave the way for future highly automated driving. Despite the importance of this problem, a similarly comprehensive and structured survey has to the best of the authors' knowledge not been developed before.

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Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions

We present an efficient asymptotically-optimal randomized motion planning algorithm solving single-query path planning problems using a bidirectional search. The algorithm combines the benefits from the widely known algorithms RRT-Connect and RRT∗ and scores better than both by finding a solution faster than RRT∗, and -unlike RRT-Connect — converging towards a theoretical optimum. We outline the proposed algorithm and proof its optimality. The efficiency and robustness is demonstrated in a number of real world applications which benefit from the bidirectional approach: planning car trajectories in a parking garage for the autonomous vehicle CoCar, generating cost-efficient trajectories for the multi-legged walking robot LAURON V in a planetary exploration scenario and performing mobile manipulation tasks for our highly actuated service robot HoLLiE. Moreover, we compare and show the improvements over "vanilla" RRT in a set of challenging benchmarks. RRT∗-Connect will contribute to increase the performance of autonomous robots and vehicles due to the reduced motion planning time in complex environments.

RRT∗-Connect: Faster, asymptotically optimal motion planning

The video-based on-road detection of vehicles at daytime allows driver assistance systems to avoid collisions and thereby improve safety, and realize comfort functions, like the well known adaptive cruise control. However, at nighttime, common video sensor based vehicle detection algorithms can't be used, because most state-of-the-art features, like shadows, symmetry and others, cannot be measured. The on-road detection of vehicles at night is an obligatory feature for modern driver assistance systems, because those systems have to provide assistance functionality at day-time and at night-time, either. In this work, vehicles in front of the own car are recognized by detection of their front or rear lights, using a perspective blob filter and subsequently searching for corresponding light pairs. For preceding vehicles, the activity of the third break light is estimated, to distinguish the maneuver state of the vehicle. Experiments show the robustness of the approach during dusk and at night sequences.

On-road vehicle detection during dusk and at night

Current advances in the research field of autonomous driving demand advanced simulation methods for testing and validation. By combining versatile foci of different simulations, we can provide an increased amount and diversity of realistic traffic scenarios, which are relevant to the development and verification of high level automated driving functions. The focus of the present paper is to propose a concept for realistic simulation scenarios, which is capable of running in different integration levels, from software- to vehicle-in-the-loop. Its application is demonstrated, exposing an experimental vehicle, which is used for autonomous driving development, to a traffic scenario with virtual vehicles on a real road network.

Testing and validating high level components for automated driving: simulation framework for traffic scenarios

The validation and verification of cognitive skills of highly automated vehicles is an important milestone for legal and public acceptance of advanced driver assistance systems (ADAS). In this paper, we present an innovative data-driven method in order to create critical traffic situations from recorded sensor data. This concept is completely contrary to previous approaches using parametrizable simulation models. We demonstrate our concept at the example of parametrizing lane change maneuvers: Firstly, the road layout is automatically derived from observed vehicle trajectories. The road layout is then used in order to detect vehicle maneuvers, which is shown exemplarily on lane change maneuvers. Then, the maneuvers are parametrized using data operators in order to create critical traffic scenarios. Finally, we demonstrate our concept using LIDAR-captured traffic situations on urban and highway scenes, creating critical scenarios out of safely recorded data.

Thomas Schamm

Papers

Testing of Advanced Driver Assistance Towards Automated Driving: A Survey and Taxonomy on Existing Approaches and Open Questions

RRT∗-Connect: Faster, asymptotically optimal motion planning

On-road vehicle detection during dusk and at night

Testing and validating high level components for automated driving: simulation framework for traffic scenarios

Data-driven simulation and parametrization of traffic scenarios for the development of advanced driver assistance systems