(1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

Human cognitive abilities: A survey of factor analytic studies

This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.

https://www.mrt.kit.edu/z/publ/download/2014/BenglerDietmayerFarberMaurerStillerWinner2014ITSM.pdf

Three Decades of Driver Assistance Systems: Review and Future Perspectives

We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Self-driving cars: A survey

In this review, we provide an overview of emerging trends and challenges in the field of intelligent and autonomous, or self-driving, vehicles. Recent advances in the field of perception, planning,...

Planning and Decision-Making for Autonomous Vehicles

For the design and test of functional modules of an automated vehicle, it is essential to define interfaces. While interfaces on the perception side, like object lists, point clouds or occupancy grids, are to a certain degree settled already, they are quite vague in the consecutive steps of context modeling and in particular on the side of driving execution. The authors consider the scene as the central interface between perception and behavior planning a control. Within the behavior planning a control block, a situation is a central data container. A scenario is a common approach to substantiate test cases for functional modules and can be used to detail the functional description of a system. However, definitions of these terms are often-at best-vague or even contradictory. This paper will review these definitions and come up with a consistent definition for each term. Moreover, we present an example for the implementation of each of these interfaces.

Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving

The Stadtpilot project aims at autonomous driving on Braunschweig's inner city ring road. For this purpose, an autonomous vehicle called “Leonie” has been developed. In October 2010, after two years of research, “Leonie's” abilities were presented in a public demonstration. This vehicle is one of the first worldwide to show the ability of driving autonomously in real urban traffic scenarios. This paper describes the legal issues and the homologation process for driving autonomously in public traffic in Braunschweig, Germany. It also dwells on the Safety Concept, the system architecture and current research activities.

/pdf/stadtpilot-first-fully-autonomous-test-drives-in-urban-4j86cqr51q.pdf

Stadtpilot: First fully autonomous test drives in urban traffic

This paper presents a functional system architecture for an automated vehicle. It provides an overall, generic structure that is independent of a specific implementation of a particular vehicle project. Yet, it has been inspired and cross-checked with a real world automated driving implementation in the Stadtpilot project at the Technische Universitat Braunschweig. The architecture entails aspects like environment and self perception, planning and control, localization, map provision, Vehicle-To-X-communication, and interaction with human operators.

Towards a Functional System Architecture for Automated Vehicles.

The development of autonomous vehicles currently focuses on the functionality of vehicle guidance systems.

/pdf/safety-concept-for-autonomous-vehicles-53g16p8ssx.pdf

Safety Concept for Autonomous Vehicles

The project Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works (aFAS) aims to develop an unmanned protective vehicle to reduce the risk of injuries due to crashes for road workers. To ensure functional safety during operation in public traffic the system shall be developed following the ISO 26262 standard. After defining the functional range in the item definition, a hazard analysis and risk assessment has to be done. The ISO 26262 standard gives hints how to process this step and demands a systematic way to identify system hazards. Best practice standards provide systematic ways for hazard identification, but lack applicability for automated vehicles due to the high variety and number of different driving situations even with a reduced functional range. This contribution proposes a new method to identify hazardous events for a system with a given functional description. The method utilizes a skill graph as a functional model of the system and an overall definition of a scene for automated vehicles to identify potential hazardous events. An adapted Hazard and Operability Analysis approach is used to identify system malfunctions. A combination of all methods results in operating scenes with potential hazardous events. These can be assessed afterwards towards their criticality. A use case example is taken from the current development phase of the project aFAS.

Andreas Reschka

Papers

Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving

Stadtpilot: First fully autonomous test drives in urban traffic

Towards a Functional System Architecture for Automated Vehicles.

Safety Concept for Autonomous Vehicles

Identification of potential hazardous events for an Unmanned Protective Vehicle