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Intelligent Anomaly Detection for Lane Rendering Using Transformer with Self-Supervised Pre-Training and Customized Fine-Tuning

Dong, Y. (author), Lu, Xingmin (author), Li, Ruohan (author), Song, Wei (author), van Arem, B. (author), Farah, H. (author)

The burgeoning navigation services using digital maps provide great convenience to drivers. However, there are sometimes anomalies in the lane rendering map images, which might mislead human drivers and result in unsafe driving. To accurately and effectively detect the anomalies, this paper transforms lane rendering image anomaly detection into...

poster 2024

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The impact of the presence and utilization policy of a dedicated lane on drivers’ preference to use automation and driving behaviour on motorways

Razmi Rad, S. (author), Farah, H. (author), Taale, Henk (author), van Arem, B. (author), Hoogendoorn, S.P. (author)

Dedicated Lanes (DLs) have been proposed as a potential alternative for the deployment of Connected and Automated Vehicles (CAVs) to facilitate platooning and increase motorway capacity. However, the impact of the presence and utilization policy of such a lane on drivers’ preference to use automation and their behaviour has not yet been...

journal article 2024

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Safety Assessment of the Interaction Between an Automated Vehicle and a Cyclist: A Controlled Field Test

Oskina, M.I. (author), Farah, H. (author), Morsink, Peter (author), Happee, R. (author), van Arem, B. (author)

The operation of automated vehicles (AVs) on shared roads requires attention concerning their interactions with vulnerable road users (VRUs), such as cyclists. This study investigates the safety of cyclists when they interact with an AV and compares it with their interaction with a conventional vehicle. Overall, 29 cyclists participated in a...

journal article 2023

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Data-driven Semi-supervised Machine Learning with Surrogate Safety Measures for Abnormal Driving Behavior Detection

Zhang, Lanxin (author), Dong, Y. (author), Farah, H. (author), Zgonnikov, A. (author), van Arem, B. (author)

Detecting abnormal driving behavior is critical for road traffic safety and the evaluation of drivers' behavior. With the advancement of machine learning (ML) algorithms and the accumulation of naturalistic driving data, many ML models have been adopted for abnormal driving behavior detection. Most existing ML-based detectors rely on (fully)...

poster 2023

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Characterizing Behavioral Differences of Autonomous Vehicles and Human-Driven Vehicles at Signalized Intersections Based on Waymo Open Dataset

Wang, Y. (author), Farah, H. (author), Yu, Rongjie (author), Qiu, Shuhan (author), van Arem, B. (author)

Autonomous vehicles (AVs) are being introduced to the traffic system with the promise of improving current traffic status. However, the empirical data also indicate contrary effects with estimated higher crash rate and change of crash patterns. Therefore, it is necessary to investigate the driving behavior of AVs and human-driven vehicles ...

journal article 2023

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Social-Aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving Through Roundabouts as a Case Study

Zhang, Li (author), Dong, Y. (author), Farah, H. (author), van Arem, B. (author)

The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs' deployment and safe driving under various maneuvers. Previous research mostly focuses on...

conference paper 2023

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Safe, Efficient, Comfort, and Energy-saving Automated Driving through Roundabout Based on Deep Reinforcement Learning

Yuan, Henan (author), Li, Penghui (author), van Arem, B. (author), Kang, Liujiang (author), Farah, H. (author), Dong, Y. (author)

Traffic scenarios in roundabouts pose substantial complexity for automated driving. Manually mapping all possible scenarios into a state space is labor-intensive and challenging. Deep reinforcement learning (DRL) with its ability to learn from interacting with the environment emerges as a promising solution for training such automated driving...

conference paper 2023

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Social-aware Planning and Control for Automated Vehicles Based on Driving Risk Field and Model Predictive Contouring Control: Driving through Roundabouts as a Case Study

Zhang, Li (author), Dong, Y. (author), Farah, H. (author), van Arem, B. (author)

The gradual deployment of automated vehicles (AVs) results in mixed traffic where AVs will interact with human-driven vehicles (HDVs). Thus, social-aware motion planning and control while considering interactions with HDVs on the road is critical for AVs’ deployment and safe driving under various maneuvers. Previous research mostly focuses on...

poster 2023

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A Hybrid Spatial-temporal Sequence-to-one Neural Network Model for Lane Detection

Dong, Y. (author), Patil, Sandeep (author), Farah, H. (author), van Arem, B. (author)

Reliable and accurate lane detection is of vital importance for the safe performance of Lane Keeping Assistance and Lane Departure Warning systems. However, under certain challenging peculiar circumstances (e.g., marking degradation, serious vehicle occlusion), it is quite difficult to get satisfactory performance in accurately detecting the...

poster 2022

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A hybrid spatial–temporal deep learning architecture for lane detection

Dong, Y. (author), Patil, Sandeep (author), van Arem, B. (author), Farah, H. (author)

Accurate and reliable lane detection is vital for the safe performance of lane-keeping assistance and lane departure warning systems. However, under certain challenging circumstances, it is difficult to get satisfactory performance in accurately detecting the lanes from one single image as mostly done in current literature. Since lane...

journal article 2022

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Behavioral adaptations of human drivers interacting with automated vehicles

Soni, Shubham (author), Reddy, N. (author), Tsapi, Anastasia (author), van Arem, B. (author), Farah, H. (author)

Advancements in technology are bringing automated vehicles (AVs) closer to wider deployment. However, in the early phases of their deployment, AVs will coexist and frequently interact with human-driven vehicles (HDVs). These interactions might lead to changes in the driving behavior of HDVs. A field test was conducted in the Netherlands with...

journal article 2022

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Will pedestrians cross the road before an automated vehicle?: The effect of drivers’ attentiveness and presence on pedestrians’ road crossing behavior

Nuñez Velasco, J.P. (author), Lee, Yee Mun (author), Uttley, Jim (author), Solernou, Albert (author), Farah, H. (author), van Arem, B. (author), Hagenzieker, Marjan (author), Merat, Natasha (author)

The impact of automated vehicles (AV) on pedestrians’ crossing behavior has been the topic of some recent studies, but findings are still scarce and inconclusive. The aim of this study is to determine whether the drivers’ presence and apparent attentiveness in a vehicle influences pedestrians’ crossing behavior, perceived behavioral control,...

journal article 2021

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The impact of a dedicated lane for connected and automated vehicles on the behaviour of drivers of manual vehicles

Razmi Rad, S. (author), Farah, H. (author), Taale, Henk (author), van Arem, B. (author), Hoogendoorn, S.P. (author)

Connected and automated vehicles (CAVs) are expected to enhance traffic efficiency by driving at shorter time headways, and traffic safety by shorter reaction times. However, one of the main concerns regarding their deployment is the mixed traffic situation, in which CAVs and manually driven vehicles (MVs) share the same road. This study...

journal article 2021

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Cyclists’ crossing intentions when interacting with automated vehicles: A virtual reality study

Nuñez Velasco, J.P. (author), de Vries, Anouk (author), Farah, H. (author), van Arem, B. (author), Hagenzieker, Marjan (author)

Most of cyclists’ fatalities originate from collisions with motorized vehicles. It is expected that automated vehicles (AV) will be safer than human-driven vehicles, but this depends on the nature of interactions between non-automated road users, among them cyclists. Little research on the interactions between cyclists and AVs exists. This...

journal article 2021

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Road Infrastructure Requirements for Improved Performance of Lane Assistance Systems

Reddy, N. (author), Farah, H. (author), Dekker, Thijs (author), Huang, Yilin (author), van Arem, B. (author)

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the road infrastructure requirements that would lead to safe operation of automated vehicles. In this context, a field test with Lane Departure Warning and...

poster 2020

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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, H. (author), Bhusari, Shubham (author), van Gent, P. (author), Mullakkal-Babu, F.A. (author), Morsink, Peter (author), Happee, R. (author), van Arem, B. (author)

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...

journal article 2020

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Adaptations in driver behaviour characteristics during control transitions from full-range Adaptive Cruise Control to manual driving: an on-road study

Varotto, S.F. (author), Farah, H. (author), Bogenberger, Klaus (author), van Arem, B. (author), Hoogendoorn, S.P. (author)

Adaptive Cruise Control (ACC) can reduce traffic congestion and accidents. In dense traffic flow conditions and when changing lanes, drivers prefer to deactivate the ACC. These control transitions between automation and manual driving could impact driver behaviour characteristics. However, few studies have analysed the magnitude and duration of...

journal article 2020

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Design and operation of dedicated lanes for connected and automated vehicles on motorways: A conceptual framework and research agenda

Razmi Rad, S. (author), Farah, H. (author), Taale, Henk (author), van Arem, B. (author), Hoogendoorn, S.P. (author)

Dedicated Lanes (DLs) have been proposed as a potential scenario for the deployment of Automated and/or Connected Vehicles (C/AVs) on the road network. However, evidence-based knowledge regarding the impacts of different design configurations, utilization policies, and the design of their access/egress on traffic safety and efficiency is...

journal article 2020

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Operational Design Domain Requirements for Improved Performance of Lane Assistance Systems: A Field Test Study in The Netherlands

Reddy, N. (author), Farah, H. (author), Huang, Yilin (author), Dekker, Thijs (author), van Arem, B. (author)

There is a pressing need for road authorities to take a proactive role in the deployment of automated vehicles on the existing road network. This requires a comprehensive understanding of the driving environment characteristics that affect the performance of automated vehicles. In this context, a field test with Lane Departure Warning (LDW) and...

journal article 2020

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The Persuasive Automobile: Design and Evaluation of a Persuasive Lane-Specific Advice Human Machine Interface

van Gent, P. (author), Farah, H. (author), Nes, Nicole Van (author), van Arem, B. (author)

Traffic congestion is a major societal challenge. By advising drivers on the optimal lane to drive, traffic flow can be improved, and congestion reduced. In this paper we describe the development of a lane-specific advice Human Machine Interface (HMI). Persuading drivers to follow an advice that is beneficial to the traffic situation, but may...

journal article 2020