Truck drivers’ behavior in encounters with vulnerable road users at intersections: Results from a test-track experiment

Crashes involving cyclists and pedestrians in Europe cause the deaths of about 7600 persons every year. Both cyclists and pedestrians are especially exposed in crashes with motorized vehicles and collisions with trucks can lead to severe injury outcomes. The two most frequent crash scenarios between trucks and these vulnerable road users (VRU) are: a) when the truck wants to turn right at an intersection, with a cyclist riding parallel and planning to cross the intersection and b) when a pedestrian crosses in front of the truck in perpendicular direction to the movement of the truck. Advanced Driver Assistance Systems (ADAS)—that are expected to prevent or mitigate these crashes—benefit from detailed information about the behavior of truck drivers. This study is a first exploration of this research area, with the aim to assess how drivers negotiate the encounters with VRUs in the two scenarios described above. Thirteen participants drove an instrumented truck on a test-track. After some baseline recordings, the drivers experienced two laps where they encountered a cyclist target and a pedestrian target crossing their path. The results show that the truck drivers adapted their kinematic and visual behavior in the laps where the VRU targets were crossing the intersection, compared to the baseline laps. The speed profiles of the drivers diverged approximately 30\ua0m from the intersection and glances were directed more often towards front right and right, during the scenario with the cyclist in comparison to baseline laps. For the scenario with the pedestrian crossing, the drivers changed their speed about 14\ua0m from the intersection and glances were directed more often towards the front center, compared to baseline laps. As a result, both the speed and distance from the intersection at the end of the maneuver were significantly different between VRU and baseline laps. Overall, the findings provide valuable information for the design of ADAS that warn the drivers about the presence of a cyclist travelling in parallel direction or that intervene to avoid a collision with a cyclist or pedestrian

How do cyclists interact with motorized vehicles at unsignalized intersections? Modeling cyclists’ yielding behavior using naturalistic data

When a cyclist\u27s path intersects with that of a motorized vehicle at an unsignalized intersection, serious conflicts may happen. In recent years, the number of cyclist fatalities in this conflict scenario has held steady, while the number in many other traffic scenarios has been decreasing. There is, therefore, a need to further study this conflict scenario in order to make it safer. With the advent of automated vehicles, threat assessment algorithms able to predict cyclists’ (other road users’) behavior will be increasingly important to ensure safety. To date, the handful of studies that have modeled the vehicle-cyclist interaction at unsignalized intersections have used kinematics (speed and location) alone without using cyclists’ behavioral cues, such as pedaling or gesturing. As a result, we do not know whether non-verbal communication (e.g., from behavioral cues) could improve model predictions. In this paper, we propose a quantitative model based on naturalistic data, which uses additional non-verbal information to predict cyclists’ crossing intentions at unsignalized intersections. Interaction events were extracted from a trajectory dataset and enriched by adding cyclists’ behavioral cues obtained from sensors. Both kinematics and cyclists’ behavioral cues (e.g., pedaling and head movement), were found to be statistically significant for predicting the cyclist\u27s yielding behavior. This research shows that adding information about the cyclists’ behavioral cues to the threat assessment algorithms of active safety systems and automated vehicles will improve safety

A Review of Research on Driving Styles and Road Safety

Objective: To outline a conceptual framework for understanding driving style and, based on this, review the state-of-the-art research on driving styles in relation to road safety.</br></br> Background: Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking. </br></br> Method: A systematic literature search was conducted, including empirical, theoretical and methodological research on driving styles related to road safety. </br></br> Results: A conceptual framework was proposed where driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorising and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and socio-cultural factors influencing driving style were reviewed. </br></br> Conclusion: Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesise these results and make useful generalisations. There is a considerable potential for increasing road safety by means of behaviour modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles. </br></br> Application: Knowledge about driving styles can be applied in programmes for modifying driver behaviour and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems

Making a few talk for the many – Modeling driver behavior using synthetic populations generated from experimental data

Understanding driver behavior is the basis for the development of many advanced driver assistance systems, and experimental studies are indispensable tools for constructing appropriate driver models. However, the high cost associated with testing is a serious obstacle in collecting large amounts of experimental data. This paper presents a methodology that can improve the reliability of results from experimental studies with a limited number of participants by creating a virtual population. Specifically, a methodology based on Bayesian inference has been developed, that generates synthetic cases that adhere to various real-world constraints and represent possible variations of the observed experimental data. The application of the framework is illustrated using data collected during a test-track experiment where truck drivers performed a right turn maneuver, with and without a cyclist crossing the intersection. The results show that, based on the speed profiles of the dataset and physical constraints, the methodology can produce synthetic speed profiles during braking that mimic the original curves but extend to other realistic braking patterns that were not directly observed. The models obtained from the proposed methodology have applications for the design of active safety systems and automated driving demonstrating thereby that the developed framework has great promise for the automotive industry