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

2017

IFAC-PapersOnLine

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This paper addresses the problem of following a vehicle with varying acceleration in a comfortable and safe manner. Our architecture consists of a nominal controller (here: model predictive control) and a safety controller. Although model predictive control attempts to keep a safe distance, it cannot formally guarantee it, due to the assumptions on the behavior of the leading vehicle. We address this problem by holding a formally verified safety controller available. Our novel mechanism gradually engages the safety maneuver since most critical situations resolve quickly. The overall approach is evaluated against real traffic data. The results show good position and velocity tracking performance, while safety and comfort are guaranteed.

Section: Braking Distance and Deceleration Profilementioning

confidence: 99%

Adaptive Cruise Control with Safety Guarantees for Autonomous Vehicles

Magdici

2017

IFAC-PapersOnLine

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“…Consequently, the safe distance d safe > 0 to a leading vehicle B l must be large enough for the ego vehicle to stop behind it if B l , at worst, performs an emergency brake with a max,l (cf. motion assumption P3) as formalized in [25].…”

Section: B Formalizing Safe Distancesmentioning

confidence: 99%

“…We briefly recall the results of [25]: The future position of a vehicle for a point in time t ≥ 0 can be described by the general motion equation…”

Section: B Formalizing Safe Distancesmentioning

confidence: 99%

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Verifying the safety of lane change maneuvers of self-driving vehicles based on formalized traffic rules

Pek

Zahn

2017 IEEE Intelligent Vehicles Symposium (IV)

2017

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Abstract-Validating the safety of self-driving vehicles requires an enormous amount of testing. By applying formal verification methods, we can prove the correctness of the vehicles' behavior, which at the same time reduces remaining risks and the need for extensive testing. However, current safety approaches do not consider liabilities of traffic participants if a collision occurs. Utilizing formalized traffic rules to verify motion plans allows this problem to be solved. We present a novel approach for verifying the safety of lane change maneuvers, using formalized traffic rules according to the Vienna Convention on Road Traffic. This allows us to provide additional guarantees that if a collision occurs, the selfdriving vehicle is not responsible. Furthermore, we consider misbehavior of other traffic participants during lane changes and propose feasible solutions to avoid or mitigate a potential collision. The approach has been evaluated using real traffic data provided by the NGSIM project as well as simulated lane changes.

“…[22], [23]), we have not found a provably correct safe distance calculation considering time delay. We first derive the solution for the case that both vehicles can have different acceleration a p and a f and later discuss in Prop.…”

Section: Safe Distancementioning

confidence: 99%

Can automated road vehicles harmonize with traffic flow while guaranteeing a safe distance?

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

Robert

2016

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Abstract-A frequently raised argument against safelydriving automated vehicles is that they would not harmonize well with traffic flow-unrealistically large headways would invite other traffic participants to cut in and thus put passengers of following automated vehicles at risk. In order to test this hypothesis, we use real data of thousands of vehicles recorded in the United States as part of the Next Generation Simulation (NGSIM) program. To study the hypothesis, we pretend each human-driven vehicle is automated: These automated vehicles drive exactly as the recorded human drivers, but they have a much smaller reaction time and thus can still drive safely in situations that are unsafe for human drivers. The main result is that in only very few cases an automated vehicle would not drive safely, although more than half of the human drivers do not keep a safe distance according to their capabilities. Thus, there is no increased risk of vehicles cutting in-and if they do, automated vehicles are only at risk in around 10% of all cases compared to around 60% for human drivers.