An Open Approach to Autonomous Vehicles

Kato, Shigeo; Takeuchi, Eijiro; Ishiguro, Yoshio; Ninomiya, Yoshiki; Takeda, Kazuya; Hamada, Takashi

doi:10.1109/mm.2015.133

Cited by 479 publications

(184 citation statements)

References 11 publications

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“…The open-source Automated Driving stack Autoware [13] has been selected as the Nominal Channel within the AD-EYE platform because it provides a full configurable stack of base functionality where all modules have been tested to be interoperable. Autoware has been successfully used in several projects with both real and virtual vehicles around the world.…”

Section: A Softwarementioning

confidence: 99%

AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts

Mohan¹,

Törngren²

2019

SAE Technical Paper Series

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Automated Driving is revolutionizing many of the traditional ways of operation in the automotive industry. The impact on safety engineering of automotive functions is arguably one of the most important changes. There has been a need to re-think the impact of the partial or complete absence of the human driver (in terms of a supervisory entity) in not only newly developed functions but also in the qualification of the use of legacy functions in new contexts. The scope of the variety of scenarios that a vehicle may encounter even within a constrained Operational Design Domain, and the highly dynamic nature of Automated Driving, mean that new methods such as simulation can greatly aid the process of safety engineering. This paper discusses the need for early verification of the Functional Safety Concepts (FSCs), details the information typically available at this stage in the product lifecycle, and proposes a co-simulation platform named AD-EYE designed for exploiting the possibilities in an industrial context by evaluating design decisions and refining Functional Safety Requirements based on a reusable scenario database.Leveraging our prior experiences in developing FSCs for Automated Driving functions, and the preliminary implementation of co-simulation platform, we demonstrate the advantages and identify the limitations of using simulations for refinement and early FSC verification using examples of types of requirements that could benefit from our methodology.

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Section: A Softwarementioning

confidence: 99%

AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts

Mohan¹,

Törngren²

2019

SAE Technical Paper Series

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“…This configuration allows the vehicle to drive and avoid obstacles on the road after taking control from the driver. Further detail is referred to [22].…”

Section: Automated Driving Systemmentioning

confidence: 99%

Development of an automated vehicle stop system for cardiac emergencies

Takahashi¹,

Tanaka²,

Takeuchi³

et al. 2017

Adv. sci. technol. eng. syst. j.

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“…The default planner in APEX is a state lattice planner incorporated in ROS and tested on a real vehicle. Figure 2 shows the planner GUI available as part of Autoware [4].…”

Section: Contributionsmentioning

confidence: 99%

APEX: Autonomous Vehicle Plan Verification and Execution

O’Kelly

Abbas

Gao

et al. 2016

SAE Technical Paper Series

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Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. In contrast, consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces state estimation errors and disturbances. Third, verification is restricted to a discretized state space with fixed-size cells; this assumption can artificially limit the set of available trajectories if the discretization is too coarse. Conversely, too fine of a discretization renders the problem intractable for automated analysis. This work presents a new verification tool, APEX, which investigates the combined action of a behavioral planner and state lattice-based motion planner to guarantee a safe vehicle trajectory is chosen. In APEX, decisions made at the behavioral layer can be traced through to the spatio-temporal evolution of the AV and verified. Thus, there is no need to create abstractions of the AV's controllers, and aggressive trajectories required for evasive maneuvers can be accurately investigated. AbstractAutonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. Consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces...

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An Open Approach to Autonomous Vehicles

Cited by 479 publications

References 11 publications

AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts

AD-EYE: A Co-Simulation Platform for Early Verification of Functional Safety Concepts

Development of an automated vehicle stop system for cardiac emergencies

APEX: Autonomous Vehicle Plan Verification and Execution

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