A Functional Architecture for Autonomous Driving

Behere, Sagar; Törngren, Martin

doi:10.1145/2752489.2752491

Cited by 79 publications

(26 citation statements)

References 8 publications

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“…Generally speaking, AVs operate on a three-phase design known as ''sense-plan-act'' which is the premise of many robotic systems [27][28][29]. A substantial challenge for AVs rests in making sense of the complex and dynamic driving environment [1,30].…”

Section: How Does the Av Work?mentioning

confidence: 99%

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

et al. 2016

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This study investigates the challenges and opportunities pertaining to transportation policies that may arise as a result of emerging autonomous vehicle (AV) technologies. AV technologies can decrease the transportation cost and increase accessibility to low-income households and persons with mobility issues. This emerging technology also has far-reaching applications and implications beyond all current expectations. This paper provides a comprehensive review of the relevant literature and explores a broad spectrum of issues from safety to machine ethics. An indispensable part of a prospective AV development is communication over cars and infrastructure (connected vehicles). A major knowledge gap exists in AV technology with respect to routing behaviors. Connectedvehicle technology provides a great opportunity to implement an efficient and intelligent routing system. To this end, we propose a conceptual navigation model based on a fleet of AVs that are centrally dispatched over a network seeking system optimization. This study contributes to the literature on two fronts: (i) it attempts to shed light on future opportunities as well as possible hurdles associated with AV technology; and (ii) it conceptualizes a navigation model for the AV which leads to highly efficient traffic circulations.

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Section: How Does the Av Work?mentioning

confidence: 99%

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

et al. 2016

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“…Methodologies and reference architecture for vehicle control system design have been proposed, e.g. by Gordon et al [16] and Behere and Törngren [17]. These methodologies include recommendations for layering for hierarchical control of various functionalities, and also compare and relate to well-known architecture control system patterns such as the subsumption architecture, NASREM [18] and 4D-RCS [19].…”

Section: B Related Workmentioning

confidence: 99%

Challenges in Architecting Fully Automated Driving; with an Emphasis on Heavy Commercial Vehicles

Mohan¹,

Törngren²,

Izosimov³

et al. 2016

2016 Workshop on Automotive Systems/Software Architectures (WASA)

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Fully automated vehicles will require new functionalities for perception, navigation and decision making -an Autonomous Driving Intelligence (ADI). We consider architectural cases for such functionalities and investigate how they integrate with legacy platforms. The cases range from a robot replacing the driver -with entire reuse of existing vehicle platforms, to a cleanslate design. Focusing on Heavy Commercial Vehicles (HCVs), we assess these cases from the perspectives of business, safety, dependability, verification, and realization.The original contributions of this paper are the classification of the architectural cases themselves and the analysis that follows. The analysis reveals that although full reuse of vehicle platforms is appealing, it will require explicitly dealing with the accidental complexity of the legacy platforms, including adding corresponding diagnostics and error handling to the ADI. The current fail-safe design of the platform will also tend to limit availability. Allowing changes to the platforms, will enable more optimized designs and fault-operational behaviour, but will require initial higher development cost and specific emphasis on partitioning and control to limit the influences of safety requirements. For all cases, the design and verification of the ADI will pose a grand challenge and relate to the evolution of the regulatory framework including safety standards.

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“…As they progress with the development of some functionality new problems arise because of uncertainty of physical world, as there are many unpredicted situations that could cause accidents. Due to this, the development of virtual simulators to test vehicle's cognitive computing [2] becomes a crucial part of the development. With this approach the perception module [3] receives input from computer-generated scenes and mathematically modelled movement patterns for pedestrians, bicycles, and other entities.…”

Section: Iintroductionmentioning

confidence: 99%

Development of ADAS perception applications in ROS and "Software-In-the-Loop" validation with CARLA simulator

et al. 2020

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Higher levels of autonomous driving are bringing sophisticated requirements and unpredicted challenges. In order to solve these problems, the set of functionalities in modern vehicles is growing in terms of algorithmic complexity and required hardware. The risk of testing implemented solutions in real world is high, expensive and time consuming. This is the reason for virtual automotive simulation tools for testing are heavily acclaimed. Original Equipment Manufacturers (OEMs) use these tools to create closed sense, compute, act loop to have realistic testing scenarios. Production software is tested against simulated sensing data. Based on these inputs a set of actions is produced and simulated which generates consequences that are evaluated. This creates a possibility for OEMs to minimize design errors and optimize costs of the vehicle production before any physical prototypes are produced. This paper presents the development of simple C++/Python perception applications that can be used in driver assistance functionalities. Using ROS as a prototyping platform these applications are validated and tested with "Software-In-the Loop" (SIL) method. CARLA simulator is used as a generator for input data and output commands of the autonomous platform are executed as simulated actions within simulator. Validation is done by connecting Autoware autonomous platform with CARLA simulator in order to test against various scenes in which applications are applicable. Vision based lane detection, which is one of the prototypes, is also tested in a real world scenario to demonstrate the applicability of algorithms developed with simulators to real-time processing

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A Functional Architecture for Autonomous Driving

Cited by 79 publications

References 8 publications

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

Autonomous vehicles: challenges, opportunities, and future implications for transportation policies

Challenges in Architecting Fully Automated Driving; with an Emphasis on Heavy Commercial Vehicles

Development of ADAS perception applications in ROS and "Software-In-the-Loop" validation with CARLA simulator

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