Deliverable 7.2. Report on methodology for balancing user acceptance, robustness and performance

The primary goal of this deliverable is to provide an overview of the methodology for acceptance testing that will be used during the tests conducted in T7.1, T7.2 and T7.3 within the PROSPECT project. The report starts with a description of the main characteristics of the most relevant accident scenarios where safety improvements are necessary. Among all use cases identified in WP3, twelve have been especially selected by the project to be implemented in the demonstrators: 9 for cyclists and 3 for pedestrians. Behaviours such as the velocity, distance and offset of the vehicle and cyclist are defined, so that the Safe Scenario, Critical Scenario and Possible Critical Scenario can be realized on the test tracks or in simulator environments. A literature review covering acceptance evaluation issues is then presented, outlining the questionnaires that are generally used to evaluate subjective measures, such as acceptance and trust. The methodology developed for Task 7.3 is then based on such questionnaires to be administered in tests and experiments that will evaluate PROSPECT systems. By using common questionnaires, this task facilitates an overall evaluation of the acceptance of all the developed functions. The methodology is presented in section 4 of this report, including a tool for data collection (LimeSurvey). This tool makes it possible for participants in evaluation studies to answer questions on various displays, to the convenience of the experimenters. In order to balance the user acceptance to the robustness and performance of the tested systems, all answers to the questionnaires will be linked to the PROSPECT functions tested and to the quality of the PROSPECT systems functioning. This methodology will be used at different times of the tests: before running a test/experiment (questionnaire 1 - participant information and questionnaire 3 - global expected acceptance of the system or a priori acceptability), during the test/experiment (questionnaire 2 - feedback on each situation) and after the test/experiment (questionnaire 3 - global acceptance of the system after having experienced it). At the end of this document, a section briefly describes all the experiments currently planned that will use the methodology within WP7. Their results will be reported in Deliverable 7.3 Report on simulator test results and driver acceptance of PROSPECT functions

Naturalistic Observations to Investigate Conflicts Between Drivers and VRUs in the PROSPECT Project

ESV 2017 - 25th International Technical Conference on the Enhanced Safety of Vehicles, DETROIT, ETATS-UNIS, 05-/06/2017 - 08/06/2017PROSPECT aims at developing a new generation of proactive safety systems to protect Vulnerable Road Users (VRUs), with an emphasis on pedestrians and cyclists. To improve sensor effectiveness, PROSPECT will expand the scope of scenarios addressed by sensors already on the market, enhancing their overall performance. Interactions between vehicles and VRUs were investigated in real traffic situations to better understand critical situations and identify factors that lead to conflicts. As a result, VRU and vehicle modelling will be more effective, allowing safety systems to react earlier, without increasing false activation rates. Accident studies highlighted the most relevant use cases, and further naturalistic observations provided information that could not be inferred from accident databases regarding these use cases, such as trajectories and kinematic data (speed, acceleration, TTC or PET) throughout the conflict evolution. Data was also collected on VRU's behaviors which forecast their intent in the near future (i.e. positional data, gestures). Lastly, naturalistic observations were used to look for correctly managed situations by the road users that could lead to false alarms in existing sensors. Two kinds of naturalistic observations were undertaken in three countries. A first data set (France and Hungary) was collected from on-site observations by infrastructure-mounted cameras. A second data set was collected by cars equipped with sensors and cameras (Hungary and Spain) to observe interactions with surrounding VRUs. Only situations of conflict with close proximity between road users both in space and time were studied. This important criterion qualified an encounter as a conflict. Low speed conflicts were excluded. Several hundred conflicts were collected, each classified according to use cases and annotated using a common grid. Different categories of parameters were investigated to describe: environmental conditions (light, precipitation, road surface, traffic density, etc.), infrastructure (layout, dedicated lanes, speed limit, Bruyas 1etc.), VRU characteristics (type, equipment, etc.), encounter (visibility, right of way, yielding, conflict management, estimated impact point, etc.), intent (head/torso orientation, gesture, flashing indicator), kinematics and trajectories. Start and end timestamps were recorded for time dependent parameters such as yielding, head movements, etc. Finally, variants of use cases were obtained to describe potential conflict evolutions and determinant factors of this evolution. As annotations of conflicts were based on subjective evaluation of observers, training was required. Although training sessions were organized, materials differed between observations which could lead to some distortion. However, including objective data such as kinematics and trajectories mitigated data validity concerns. Severity of conflicts, for example, was first assessed by subjective measure (as filtering process), then revised by taking into account kinematic data as a more objective measure. We also considered inconsistent accuracy level of video processing algorithms for spatial data (trajectories and kinematics)

Deliverable 2.1. Accident Analysis, Naturalistic Observations and Project Implications - Part B. Naturalistic Observations

Deliverable D2.1 'Accident Analysis, Naturalistic Observations and Project Implications' is issued in the scope of WP2 'Accident analysis and user needs' from the PROSPECT project. The objective of WP2 is to generate the user requirements for next generation proactive safety systems, with a focus on the specific needs of vulnerable road users (VRUs). Part A of deliverable D2.1 (Accident data analyses) provided results from task T2.1 'Characteristics of vehicle to VRU accidents'. Within this task, an in-depth accident analysis involving Vulnerable Road Users was carried out in Europe, focusing mainly on pedestrians and cyclists.The output already obtained from task T2.1 has provided information about the current safety situation and the identification of the most relevant car-to-cyclist and car-to-pedestrian accident scenarios where safety improvements are necessary. This data has been used to define the use cases of PROSPECT, and the system development will focus on the most relevant of these. The overall process of use case definition for PROSPECT and the associated test catalogue derived from the accident analysis data is provided in deliverable D3.1 'The addressed VRU scenarios within PROSPECT and associated test catalogue', available in May 2016.This report corresponds to Part B of deliverable D2.1, which seeks to provide additional knowledge to the project through naturalistic observations within selected European cities in order to establish how vehicles and VRUs interact in real traffic situations. This work has been developed in task T2.2.Naturalistic observations facilitate a better understanding of potentially dangerous traffic situations with VRUs. In particular, it includes the identification of motions, behaviours and interactions that lead to such situations, from both VRU and driver perspective. Additional to the information provided from the accident databases, it is necessary to identify the parameters that signal VRU intent in order to enable earlier and more precise reactions by safety systems. Naturalistic observations are therefore crucial for the development of advanced algorithms integrated in next generation PROSPECT-like systems, and must be also taken into account as relevant factors for the definition of test scenarios.An introduction and specific objectives of the task are presented in this part, as well as the methodology for data acquisition and extraction of conflicts regarding VRUs in real-world traffic from infrastructure-mounted and/or vehicle-based sensors and cameras in Lyon, Budapest and Barcelona. An additional study made on Helmond on cyclist behaviour is also described.The parameters considered for the analyses of conflicts are provided, as well as analysis of the conflicts.Finally, this part of the document offers a general conclusion about the results obtained from the naturalistic observations