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Part I of this paper proposed a development process and a system platform for the development of autonomous cars based on a distributed system architecture. The proposed development methodology enabled the design and development of an autonomous car with benefits such as a reduction in computational complexity, fault-tolerant characteristics, and system modularity. In this paper (Part II), a case study of the proposed development methodology is addressed by showing the implementation process of an autonomous driving system. In order to describe the implementation process intuitively, core autonomous driving algorithms (localization, perception, planning, vehicle control, and system management) are briefly introduced and applied to the implementation of an autonomous driving system. We are able to examine the advantages of a distributed system architecture and the proposed development process by conducting a case study on the autonomous system implementation. The validity of the proposed methodology is proved through the autonomous car A1 that won the 2012 Autonomous Vehicle Competition in Korea with all missions completed.

An autonomous car is a self-driving vehicle that has the capability to perceive the surrounding environment and navigate itself without human intervention. For autonomous driving, complex autonomous driving algorithms, including perception, localization, planning, and control, are required with many heterogeneous sensors, actuators, and computers. To manage the complexity of the driving algorithms and the heterogeneity of the system components, this paper applies distributed system architecture to the autonomous driving system, and proposes a development process and a system platform for the distributed system of an autonomous car. The development process provides the guidelines to design and develop the distributed system of an autonomous vehicle. For the heterogeneous computing system of the distributed system, a system platform is presented, which provides a common development environment by minimizing the dependence between the software and the computing hardware. A time-triggered network protocol, FlexRay, is applied as the main network of the software platform to improve the network bandwidth, fault tolerance, and system performance. Part II of this paper will provide the evaluation of the development process and system platform by using an autonomous car, which has the ability to drive in an urban area.

The FlexRay protocol has emerged as the de facto standard for automotive communication systems, and it offers the advantages of both the event-triggered and time-triggered paradigms. To minimize bandwidth consumption (BC) in the static segment of the FlexRay communication cycle, we propose a frame-packing algorithm called BC minimizing with various periodic signals (BCMVPS), which is formulated as generalized integer linear programming that allows the packing of signals with different periods into a message frame. We also present the lower and upper bounds of the size of a static slot, as well as present a heuristic called BC best fit decreasing for linear frame selection for reducing the computing time of the BCMVPS algorithm. We compare the performance of the proposed algorithms with existing algorithms using the Society of Automotive Engineers benchmark data and GNU linear programming kit. The experimental results show that the BC of the proposed algorithms is less than that of the existing frame-packing algorithms.

This paper describes a new FlexRay network parameter optimization (NPO) method consisting of two straightforward steps: design the lengths of the static (ST) slot and the communication cycle. In the first step, we formulate an optimal problem for designing the ST slot length. The bandwidth limitation, composed of protocol overhead and unused network resources, is considered as optimal criterion for efficient network usage. With the exploration of design space (i.e., ST slot lengths which satisfy the constraints of the FlexRay specifications), the optimal ST slot length is determined. Based on the result of the first step, the optimal communication-cycle length is designed in the next step. In this step, we proposed an algorithm for analyzing the worst case response times (WCRTs) for the ST and dynamic frames. Using this algorithm, the optimal communication-cycle length with minimum network delays is derived. The NPO method formalizes the FlexRay network configuration process in two steps for simplicity. Furthermore, the method enables the efficient usage of FlexRay bus with minimum WCRT. Finally, vehicle chassis control system based on FlexRay network is designed using the proposed NPO method. In this example system, we verified the parameter-optimization algorithm, and analysis results are confirmed.

There has been an increasing interest in using star topologies in field-bus communications, e.g., in Time Triggered Protocol for SAE classC applications (TTP/C), FlexRay, or controller area network (CAN), due to increased fault resilience and potential error-containment advantages. In this context, an innovative CAN-compliant star topology, CANcentrate, has been developed, whose hub includes enhanced fault-treatment mechanisms. However, despite this interest toward stars, it is still necessary to quantify their real dependability benefits. For this purpose and for the particular case of CAN, this paper presents models for the dependability features of CAN and CANcentrate using Stochastic Activity Networks (SANs). It quantitatively compares their reliability and error-containment capabilities under permanent hardware faults. These models rely on assumptions that ensure that results are not biased toward CANcentrate, which, in some cases, is too detrimental for it. Thus, despite not reflecting the full CANcentrate potential, results quantitatively confirm the improvement of error-containment it achieves over CAN. Additionally, the way in which the nodes' ability to contain their own errors affects the relevance of using a star topology has been quantified. Although this paper refers to the case of CAN, conclusions regarding the justification of using a star topology depending on this ability can be extrapolated to other field-bus technologies.

This paper presents the design, implementation, and validation of a FlexRay transceiver and a SpaceWire (SpW) router and interface, which constitute the main hardware building blocks of the two in-vehicle communication standards. The FlexRay protocol features data rates up to 10 Mb/s and time- and event-triggered transmissions, along with scalable fault-tolerance support, and it is expected to become the standard network for X-by-wire and active safety automotive systems. However, collision avoidance and driver-assistance applications based on camera/radar sensors require data rates up to hundreds of megabits per second as well as fault tolerance, features that can hardly be covered by current or expected automotive standards. In this scenario, a promising technology seems to be the new SpW protocol, currently used in avionics and aerospace.