Abstract

With the aim of assessing the extent and nature of naturalistic studies involving vulnerable road users, a systematic literature review was carried out. The purpose of this review was to identify studies based on naturalistic data from VRUs (pedestrians, cyclists, moped riders and motorcyclists) to provide an overview of how data was collected and how data has been used. In the literature review, special attention is given to the use of naturalistic studies as a tool for road safety evaluations to gain knowledge on methodological issues for the design of a naturalistic study involving VRUs within the InDeV project. The review covered the following types of studies: • Studies collecting naturalistic data from vulnerable road users (pedestrians, cyclists, moped riders, motorcyclists). • Studies collecting accidents or safety-critical situations via smartphones from vulnerable road users and motorized vehicles. • Studies collecting falls that have not occurred on roads via smartphones. Four databases were used in the search for publications: ScienceDirect, Transport Research International Documentation (TRID), IEEE Xplore and PubMed. In addition to these four databases, six databases were screened to check if they contained references to publications not already included in the review. These databases were: Web of Science, Scopus, Google Scholar, Springerlink, Taylor & Francis and Engineering Village. The findings revealed that naturalistic studies of vulnerable road users have mainly been carried out by collecting data from cyclists and pedestrians and to a smaller degree of motorcyclists. To collect data, most studies used the built-in sensors of smartphones, although equipped bicycles or motorcycles were used in some studies. Other types of portable equipment was used to a lesser degree, particularly for cycling studies. The naturalistic studies were carried out with various purposes: mode classification, travel surveys, measuring the distance and number of trips travelled and conducting traffic counts. Naturalistic data was also used for assessment of the safety based on accidents, safety-critical events or other safety-related aspect such as speed behaviour, head turning and obstacle detection. Only few studies detect incidents automatically based on indicators collected via special equipment such as accelerometers, gyroscopes, GPS receivers, switches, etc. for assessing the safety by identifying accidents or safety-critical events. Instead, they rely on self-reporting or manual review of video footage. Despite this, the review indicates that there is a large potential of detecting accidents from naturalistic data. A large number of studies focused on the detection of falls among elderly people. Using smartphone sensors, the movements of the participants were monitored continuously. Most studies used acceleration as indicator of falls. In some cases, the acceleration was supplemented by rotation measurements to indicate that a fall had occurred. Most studies of using kinematic triggers for detection of falls, accidents and safety-critical events were primarily used for demonstration of prototypes of detection algorithms. Few studies have been tested on real accidents or falls. Instead, simulated falls were used both in studies of vulnerable road users and for studies of falls among elderly people. Document type: Report

Abstract

To develop automated systems that can execute acceptable and comfortable lane changes on the motorway, it is useful to understand how lane changes are performed by human drivers, what factors influence the lane changes, and how they can be described parametrically. More specifically, it is important to understand the acceptable thresholds for parameters which are associated with the manoeuvre execution from a driver’s perspective. Furthermore, the cognitive process of the intention and decision for a lane change needs to be addressed to provide a well-accepted automated lane change decision and timing. This work aims at gaining first qualitative insights into these aspects. Two small-scale on-road studies were conducted to initially collect data on the process of human lane change behaviour in different driving scenarios. Objective data has been collected by equipping a measurement vehicle with a broad sensor set. Qualitative subjective data was gathered through different survey methods. In the first study, both the driver and a co-driver were included by means of questionnaires. In the second study, the ‘thinking aloud’ method was utilised to gain a deeper insight into the driver’s cognitive process. Based on the analysis of the objective data a parametric description of the lane change profile has been established. They can be used to generate automated lane change trajectories based on parameters from real data. For the evaluation of such automated lane change profiles, the implementation of driving studies in a highdynamic driving simulator is useful and has been initially tested in an expert pilot study