Formalising Traffic Rules for Accountability of Autonomous Vehicles

Rizaldi, Albert; Althoff, Matthias

doi:10.1109/itsc.2015.269

Cited by 75 publications

(61 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Our checker could also be extended with reachability analysis [2,13] in order to verify a continuous trace. Lastly, aligned with our previous work in formalisation of traffic rules [23], we wish to increase the number of formalised traffic rules such that the liability issue can be deduced automatically with our checkers.…”

Section: Discussionmentioning

confidence: 99%

“…For example, who should be held liable when a collision involving an autonomous vehicle occurs? In our previous paper [23], we proposed to solve this issue by formalising vehicles' behaviours and traffic rules in higher-order logic (HOL). This formalisation allows us to check formally whether an autonomous vehicle complies with traffic rules.…”

Section: Introductionmentioning

confidence: 99%

“…We follow our previous design decision [23] to use Isabelle/HOL [18] for three reasons. Firstly, it has rich libraries of formalised real analysis which is required to turn the descriptive definition of safe distance into the prescriptive one.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Formally Verified Checker of the Safe Distance Traffic Rules for Autonomous Vehicles

Rizaldi

Immler

Althoff

2016

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. One barrier in introducing autonomous vehicle technology is the liability issue when these vehicles are involved in an accident. To overcome this, autonomous vehicle manufacturers should ensure that their vehicles always comply with traffic rules. This paper focusses on the safe distance traffic rule from the Vienna Convention on Road Traffic. Ensuring autonomous vehicles to comply with this safe distance rule is problematic because the Vienna Convention does not clearly define how large a safe distance is. We provide a formally proved prescriptive definition of how large this safe distance must be, and correct checkers for the compliance of this traffic rule. The prescriptive definition is obtained by: 1) identifying all possible relative positions of stopping (braking) distances; 2) selecting those positions from which a collision freedom can be deduced; and 3) reformulating these relative positions such that lower bounds of the safe distance can be obtained. These lower bounds are then the prescriptive definition of the safe distance, and we combine them into a checker which we prove to be sound and complete. Not only does our work serve as a specification for autonomous vehicle manufacturers, but it could also be used to determine who is liable in court cases and for online verification of autonomous vehicles' trajectory planner.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Formally Verified Checker of the Safe Distance Traffic Rules for Autonomous Vehicles

Rizaldi

Immler

Althoff

2016

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…We denote the time-varying, free drivable space on the road surface as W S,free (t) ⊂ R 2 and introduce O(x(t)) : R n → P (R 2 ) (P () returns the power set) as the function that returns the occupancy of a vehicle given its state. A possible solution has to ensure that the occupancy of the vehicle is in the free space (∀t ∈ [t 0 , t f ] : O(x(t)) ∈ W S,free (t)) and respects additional constraints g S (x(t), u(t), t) ≤ 0 provided by scenario S, such as speed limits or other traffic rules [25]. Equality constraints can be constructed from inequality constraints (e.g.…”

Section: B Motion Planning Problemmentioning

confidence: 99%

CommonRoad: Composable benchmarks for motion planning on roads

Althoff

Koschi

Manzinger

2017

2017 IEEE Intelligent Vehicles Symposium (IV)

Self Cite

285

166

View full text Add to dashboard Cite

Abstract-Numerical experiments for motion planning of road vehicles require numerous components: vehicle dynamics, a road network, static obstacles, dynamic obstacles and their movement over time, goal regions, a cost function, etc. Providing a description of the numerical experiment precise enough to reproduce it might require several pages of information. Thus, only key aspects are typically described in scientific publications, making it impossible to reproduce results-yet, reproducibility is an important asset of good science. Composable benchmarks for motion planning on roads (CommonRoad) are proposed so that numerical experiments are fully defined by a unique ID; all information required to reconstruct the experiment can be found on the CommonRoad website. Each benchmark is composed by a vehicle model, a cost function, and a scenario (including goals and constraints). The scenarios are partly recorded from real traffic and partly hand-crafted to create dangerous situations. We hope that CommonRoad saves researchers time since one does not have to search for realistic parameters of vehicle dynamics or realistic traffic situations, yet provides the freedom to compose a benchmark that fits one's needs.

show abstract

“…The main challenge is that traffic rules contained in legal texts are mostly written in natural language and are thus often abstract and imprecise. A history of the attempts at formalizing traffic rules and the development of a new approach to the task using Isabelle/HOL are available in [22].…”

Section: Logical Artificial Intelligence In Control Strategy Of Autonmentioning

confidence: 99%

Experimental Autonomous Road Vehicle with Logical Artificial Intelligence

Shadrin¹,

Varlamov²,

Ivanov³

2017

Journal of Advanced Transportation

View full text Add to dashboard Cite

This article describes some technical issues regarding the adaptation of a production car to a platform for the development and testing of autonomous driving technologies. A universal approach to performing the reverse engineering of electric power steering (EPS) for the purpose of external control is also presented. The primary objective of the related study was to solve the problem associated with the precise prediction of the dynamic trajectory of an autonomous vehicle. This was accomplished by deriving a new equation for determining the lateral tire forces and adjusting some of the vehicle parameters under road test conductions. A Mivar expert system was also integrated into the control system of the experimental autonomous vehicle. The expert system was made more flexible and effective for the present application by the introduction of hybrid artificial intelligence with logical reasoning. The innovation offers a solution to the major problem of liability in the event of an autonomous transport vehicle being involved in a collision.

show abstract

Formalising Traffic Rules for Accountability of Autonomous Vehicles

Cited by 75 publications

References 16 publications

A Formally Verified Checker of the Safe Distance Traffic Rules for Autonomous Vehicles

A Formally Verified Checker of the Safe Distance Traffic Rules for Autonomous Vehicles

CommonRoad: Composable benchmarks for motion planning on roads

Experimental Autonomous Road Vehicle with Logical Artificial Intelligence

Contact Info

Product

Resources

About