AaronFu的博客

hypervisor is the solution for automotive software development, in the new E/E architecture trend of convergence (like Domain Control Unit, or Zonal Control Unit). it provides the possibility to run several OS on a hardware structure. thus provides the convenience of software functions split, and Software modular development based on functional safety standards.

Auto cybersecurity is on national agendas because automobiles are increasingly connected to the Internet and other systems and bad actors can commandeer a vehicle and render it dangerous, amongst other undesirable outcomes. The problem is complex and the point-solutions that exist today are fragmented leaving a very porous and “hackable” system. BlackBerry provides a 7-Pillar recommendation to harden automobile electronics from attack. The solution is intended to make it significantly harder for an attacker to create mischief. This paper describes the 7-pillars and how BlackBerry can help.

This position paper introduces the concept of ar- tificial “co-drivers” as an enabling technology for future intel- ligent transportation systems. In Sections I and II, the design principles of co-drivers are introduced and framed within gen- eral human–robot interactions. Several contributing theories and technologies are reviewed, specifically those relating to relevant cognitive architectures, human-like sensory-motor strategies, and the emulation theory of cognition. In Sections III and IV, we present the co-driver developed for the EU project interactIVe as an example instantiation of this notion, demonstrating how it conforms to the given guidelines. We also present substantive experimental results and clarify the limitations and performance of the current implementation. In Sections IV and V, we analyze the impact of the co-driver technology. In particular, we identify a range of application fields, showing how it constitutes a universal enabling technology for both smart vehicles and cooperative systems, and naturally sets out a program for future research.

Multi-processor system on chip (MPSoC) has been widely applied in embedded systems in the past decades. However, it has posed great challenges to efficiently design and implement a rapid prototype for diverse applications due to heterogeneous instruction set architectures (ISA), programming interfaces and software tool chains. In order to solve the problem, this paper proposes a novel high level architecture support for automatic out-of-order (OoO) task execution on FPGA based heterogeneous MPSoCs. The architecture support is composed of a hierarchical middleware with an automatic task level OoO parallel execution engine. Incorporated with a hierarchical OoO layer model, the middleware is able to identify the parallel regions and generate the sources codes automatically. Besides, a runtime middleware Task-Scoreboarding analyzes the inter-task data dependencies and automatically schedules and dispatches the tasks with parameter renaming techniques. The middleware has been verified by the prototype built on FPGA platform. Examples and a JPEG case study demonstrate that our model can largely ease the burden of programmers as well as uncover the task level parallelism.

In this paper, we present a hierarchical mapping method that allows us to obtain accurate metric maps of large en- vironments in real time. The lower (or local) map level is composed of a set of local maps that are guaranteed to be statistically inde- pendent. The upper (or global) level is an adjacency graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained at this level in a relative stochastic map. We propose a close to optimal loop closing method that, while maintaining independence at the local level, imposes consistency at the global level at a computational cost that is linear with the size of the loop. Experimental results demonstrate the efficiency and precision of the proposed method by mapping the Ada Byron building at our campus. We also analyze, using simulations, the precision and convergence of our method for larger loops.

The technological advancements of recent years have steadily increased the complexity of vehicle-internal software systems, and the ongoing development towards autonomous driving will further aggravate this situation. This is leading to a level of complexity that is pushing the limits of existing vehicle software architectures and system designs. By changing the software structure to a service-based architecture, companies in other domains successfully managed the rising complexity and created a more agile and future-oriented development process. This paper presents a case-study investigating the feasibility and possible effects of changing the software architecture for a complex driver assistance function to a microservice archi- tecture. The complete procedure is described, starting with the description of the software-environment and the corresponding requirements, followed by the implementation, and the final testing. In addition, this paper provides a high-level evaluation of the microservice architecture for the automotive use-case. The results show that microservice architectures can reduce complexity and time-consuming process steps and make the automotive software systems prepared for upcoming challenges as long as the principles of microservice architectures are carefully followed.

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.

Advancing to higher levels of driving automation brings unpredicted challenges and with them, many situations that cannot be foreseen. In order to overcome these problems, set of functionalities in modern vehicle is growing in terms of algorithmic complexity and required hardware. Risk of testing implemented solutions in real world is high, expensive and time consuming. That is why virtual simulation tools for automotive testing are heavily acclaimed. Original Equipment Manufacturers (OEMs) use these tools to create closed sense, compute, act loop. Production software is tested against simulated sensing data and simulated action consequences are generated according to the given software commands. This gives OEMs ability to optimize design of the vehicles before any physical prototypes are produced. Early optimization brings reduced costs and less time delays. This paper presents development of simple C++ perception applications using ROS as a prototyping platform that are validated and tested with “Software-In-the-Loop” (SIL) methods. Simulations are created using CARLA simulator to provide data and transform commands given by the autonomous platform into simulated actions. Validation is done by connecting Autoware autonomous platform with CARLA simulator in order to test against various scenes in which applications are applicable.

自动驾驶架构设计，IEEE论文 Architecture design is one of the most important problems for an intelligent system. In this paper, a practical framework of hardware and software is proposed to reveal the external configuration and internal mechanism of an autonomous vehicle—a typical intelligent system. The main contributions of this paper are as follows. First, we compare the advantages and disadvantages of three typical sensor plans and introduce a general autopilot for a vehicle. Second, we introduce a software architecture for an autonomous vehicle. The perception and planning performances are improved with the help of two inner loops of simultaneous localization and mapping. An algorithm to enlarge the detection range of the sensors is proposed by adding an inner loop to the perception system. A practical feedback to restrain mutations of two adjacent planning periods is also realized by the other inner loop. Third, a cross-platform virtual server (named project cocktail) for data transmission and exchange is presented in detail. Through comparisons with the robot operating system, the performance of project cocktail is proven to be considerably better in terms of transmission delay and throughput. Finally, a report on an autonomous driving test implemented using the proposed architecture is presented, which shows the effectiveness, flexibility, stability, and low-cost of the overall autonomous driving system.

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