Probabilistic field approach for motorway driving risk assessment

Mullakkal-Babu, Freddy Antony; Wang, Meng; He, Xiaolin; Arem, Bart van; Happee, Riender

doi:10.1016/j.trc.2020.102716

Cited by 59 publications

(40 citation statements)

References 31 publications

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“…Second, we employ our new Probabilistic Driving Risk Field (PRDF), which is predictive in nature and captures the motion uncertainties of surrounding vehicles. PRDF was shown to be more generic than existing surrogate measures of safety in simple scenarios [37], and the current paper demonstrates that PRDF can be effectively used to analyze conflicts in large scale traffic simulations. Last, the simulation results of the two generic design strategies of ADS, being reactive and predictive, give new insights in the potential benefits and risks of such systems at the collective traffic level.…”

Section: Objective Contribution and Structurementioning

confidence: 87%

“…Such a measure would allow a straightforward comparison of maneuver safety. Based on field-theory, we proposed an approach to assess the driving risk: the risk taken by a vehicle as a result of its interaction with adjacent entities [37]. This approach-Probabilistic Driving Risk Field (PDRF)-can describe the driving risk in interaction with both vehicles and road-side barriers.…”

Section: Safety Metricsmentioning

confidence: 99%

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Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

Mullakkal-Babu

Wang

Arem

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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We present a simulation-based approach to assess the safety impacts of vehicles equipped with Automated Driving Systems (ADS) in mixed traffic with Human-driven Vehicles (HV). Specifically, we compare two generic longitudinal strategies of ADS to handle a cut-in: Reactive ADS acting only when the cut-in vehicle crosses the target lane boundary, and Predictive ADS acting at the onset of the cut-in manoeuvre. We identify their distinctive effects on the traffic safety under cut-in maneuvers of adjacent human-driven vehicles at highway merges. We employ a microscopic traffic flow simulator that describes the lane changing process with high detail, accounting for the vehicle interaction and consequent trajectory updates. These high-resolution trajectories are post-processed to estimate a set of relevant surrogate measures of safety. By analyzing these measures, we find that the predictive ADS significantly outperforms the reactive ADS in aspects such as temporal proximity to crash, expected crash severity and the driving risk (combining the two aspects), and the number of aborted lane changes by HV. The negative safety impact of reactive ADS becomes prominent at penetration rate > 10%. The major difference between the two ADS approaches appears in the dynamics of risk during the lane changing. When a vehicle cuts in ahead of Reactive ADS, the risk peaks approximately halfway through the maneuver; whereas with Predictive ADS the risk remains marginal throughout. This work demonstrates the potential of simulation-based safety assessment to differentiate the safety impacts of automation functionalities at an early stage of product development.

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Section: Objective Contribution and Structurementioning

confidence: 87%

Section: Safety Metricsmentioning

confidence: 99%

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

Mullakkal-Babu

Wang

Arem

et al. 2022

IEEE Trans. Intell. Transport. Syst.

Self Cite

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“…The analysis method was composed of an objective and subjective driving risk assessment. The objective driving risk assessment was based on the Probabilistic Driving Risk Field (PDRF) method [16], [17], while the subjective risk assessment was based on driver behavior, trust and situation awareness when driving with the LKAS.…”

Section: Analysis Methodsmentioning

confidence: 99%

“…Other measures used to assess the risk of lane-keeping performance include the Time to Lane Departure (TLD)/ Time to Lane Crossing (TLC), see for example Tarko [15] and Li et al [12]. Another recently newly developed and applied risk measure is the Probabilistic Driving Risk Field (PDRF), [16], [17]. This is based on the concept that safety, or rather, 'unsafety' can be regarded as a combination of risk and its consequence.…”

Section: Assessment Of the Lkas Performancementioning

confidence: 99%

“…In the PDRF method, risk is defined by the magnitude of the consequences of a collision and the chance of its occurrence. The Potential Risk Field (PRF) first proposed and defined by Wang, et al [25] and later by Mullakkal-Babu et al [17] is composed of the risks experienced due to the non-moving road entities such as lane marking, guard rails, road medians. Lanemarking, in this case is a virtual obstacle that does not involve in a collision but impose a behavioral constraints on driving.…”

Section: A Objective Risk Assessment (Potential Risk Field)mentioning

confidence: 99%

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An Empirical Analysis to Assess the Operational Design Domain of Lane Keeping System Equipped Vehicles Combining Objective and Subjective Risk Measures

Farah

Bhusari

Gent

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Lower levels of automation are designed to work in specific conditions referred to as the Operational Design Domain (ODD). Beyond these conditions, the human driver is expected to take control. A mismatch between a driver's understanding and expectations of the automated vehicle capabilities and its actual capabilities as prescribed in the Original Equipment Manufacturers (OEMs) manual, could affect their safety and trust in automation. The main aim of this study is to develop a method for assessing the ODD of lane keeping system equipped vehicles. The analysis method is composed of an objective driving risk measure based on the Probabilistic Driving Risk Field (PDRF), and a subjective risk measure based on driver behavior, trust and situation awareness. We demonstrate the method applicability using the Automated Lane Keeping system of the Tesla Model S. A field test was conducted with 19 participants on public roads in the Netherlands including situations within and outside the defined ODD by the OEM. Across all test situations, a mismatch was observed between the ODD specified by the OEM and by the driver. Situations outside the ODD (i.e. no-lane markings and on/off-ramp) were often regarded as within the ODD by the participants. Situations inside the ODD (i.e. tunnel and curve) were mostly correctly classified by the participants. This analysis method has the potential to aid OEMs and road operators in defining more clearly the ODD while taking into account the driver's safety and awareness of the system capabilities.

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Effect of Different Secondary Tasks Types on the Driving Safety of Older Drivers

Xia

Liu

Wang

et al. 2021

Lecture Notes in Computer Science

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Probabilistic field approach for motorway driving risk assessment

Cited by 59 publications

References 31 publications

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

Comparative Safety Assessment of Automated Driving Strategies at Highway Merges in Mixed Traffic

An Empirical Analysis to Assess the Operational Design Domain of Lane Keeping System Equipped Vehicles Combining Objective and Subjective Risk Measures

Effect of Different Secondary Tasks Types on the Driving Safety of Older Drivers

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