AIM Research Intersection: Instrument for traffic detection and behavior assessment for a complex urban intersection

The Research Intersection as part of Test field AIM (Application Platform for Intelligent Mobility) is a field instrument for detection and assessment of traffic behavior for a complex urban intersection in the city of Braunschweig, Germany. It serves as tool for the purpose of analyzing natural traffic behavior and phenomena, e.g. in safety related traffic situations, based on empirically observed trajectories. Thus, the facility can be used for a number of applications in the field of intelligent mobility services

Detection and Analysis of Critical Interactions in Illegal U-turns at an Urban Intersection

Before Advanced Driver Assistance Systems (ADAS) can guide vehicles through real-world traffic, it has to be ensured that they will operate reliably in normal, but particularly in rare and critical situations such as traffic conflicts or near misses under all circumstances and conditions. To test the ADAS functions in rare critical situations, this study aims to gather knowledge about such situations i.e. detect them at an urban intersection, analyze the road user behavior and describe relevant kinematic patterns based on an aggregated long-term analysis. To limit the number of possible situations, we focus on interactions between illegally U-turning motorized road users (MRU) and vulnerable road users (VRU). Since trajectory and video data of traffic violations are rare, the relevant trajectories of MRUs and VRUs need to be identified first. Therefore, virtual loops are employed, which are placed at the expected starts and ends of the trajectories. All trajectories that intersect both, the start and end loop, are extracted from the dataset. Then, the resulting trajectories have to be evaluated regarding driving paths, interaction, and criticality. For this purpose, the surrogate measure of safety "post encroachment time" (PET) is applied. Afterward, available scene videos are used to evaluate the PET-triggered situations as critical or uncritical encounters. Finally, descriptive and inferential statistical methods are applied to kinematic data of those trajectory pairs to identify relevant behavioral patterns of the road users. The examined dataset was recorded at the Application Platform for Intelligent Mobility Research Intersection of the German Aerospace Center in Brunswick, Germany. Applying the beforementioned methodology to the dataset yielded the detection of relevant interactions. The kinematic patterns of the interactions that were assessed as critical close encounters were further analyzed to derive situational patterns. Based on this analysis it can be shown that the reason for critical situations was that the U-turning MRU had to leave the intersection. Thus, we can validate that the road safety for vehicles leaving the intersection in an unallowed direction can become critical. To understand these situations in detail they are described in the following. The U-turning MRUs use the lane of the left turning vehicle and have to let the oncoming traffic pass before they can execute their turning maneuver. While the median U-turn curve radius is 7.6 m other curve radii vary between 2.8 and 22.3 m. Some U-turning vehicles that enter the intersection during the red phase of the VRU are waiting so long for the oncoming vehicles to pass that the traffic light for the VRUs is already switching to green when the U-turning vehicle leaves the intersection. Based on the PET-triggered situations and their video scenes we could identify and evaluate critical U-turn situations. Our analysis showed, that these situations occur when the vehicles had to wait a long time at the intersection and had to leave it at a time when the traffic lights gave the right of way to the VRUs that were crossing the lane. In a conclusion, tailored preventive measures such as vehicle-to-infrastructure communication could reduce criticality in such U-turn situations because the vehicles would then be aware of the traffic light state. The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project Methoden und Maßnahmen zur Absicherung von KI basierten Wahrnehmungsfunktionen für das automatisierte Fahren (KI-Absicherung). The authors would like to thank the consortium for the successful cooperation

Extraction and Analysis of Highway On-Ramp Merging Scenarios from Naturalistic Trajectory Data

Connected and Automated Vehicles (CAVs) are envisioned to transform the future industrial and private transportation sectors. However, due to the system's enormous complexity, functional verification and validation of safety aspects are essential before the technology merges into the public domain. Therefore, in recent years, a scenario-driven approach has gained acceptance, emphasizing the requirement of a solid data basis of scenarios. The large-scale research facility Test Bed Lower Saxony (TFNDS) enables the provision of ample information for a database of scenarios on highways. For that purpose, however, the scenarios of interest must be identified and extracted from the collected Naturalistic Trajectory Data (NTD). This work addresses this problem and proposes a methodology for onramp scenario extraction, enabling scenario categorization and assessment. An Hidden Markov Model (HMM) and Dynamic Time Warping (DTW) is utilized for extraction and a decision tree with the Surrogate Measure of Safety (SMoS) Post Enroachment Time (PET) for categorization and assessment. The efficacy of the approach is shown with a dataset of NTD collected on the TFND

A collaborative framework for semi-automatic scenario-based mining of big road user data

Traffic research has benefited from a significant expansion in the amount of available data. Consequently, the need arises for an automatic and efficient method to extract and analyze relevant traffic situations instead of a more traditional and manual approach like manual video annotation. This paper presents a framework to create such a data pipeline. The user must define the target scenarios and the pipeline will abstract the available trajectory data into candidate scenes (groups of interacting trajectories) and select the matches for the target scenarios. These scenes will be mined and modelled automatically for new valuable information. Furthermore, Surrogate Measures of Safety (SMoS) are applied to identify the critical and atypical scenes of the target scenarios. A set of eight scenarios containing interactions between bicycles and MRUs (Motorized Road Users) at the AIM (Application Platform for Intelligent Mobility) Research Intersection in the city of Braunschweig, Germany, was mined by a team of three researchers using the presented framework to validate it with positive results

Criticality dimension-based probabilistic framework to detect near crashes in a roundabout

Background: Preventing fatal traffic accidents towards Vision Zero is a challenge for the society. The collection of critical events from video recorded traffic data is of essential value for a better understanding on how and under what circumstances critical situations evolve. Identified behavioral patterns and derived infrastructural measures cannot only help to make driving safer, but also help to mature automated driving functions (ADFs) to make automated vehicles drive and interact more like humans especially in challenging situations. One flaw when developing ADFs is the dependency on synthetic simulated traffic scenarios. Method: In this paper, a novel probability-based framework is proposed allowing to measure the degree of criticality C(d) based on two dimensions explaining risk: severity (delta-v) and proximity (distance). Results: This metric is applied on real data of a roundabout. An initial evaluation of it was conducted using both a novel proposed method that takes the reaction of the second vehicle merged into account, and a practical application that shows a potential correlation between the traffic expert’s perceived risk and the metric. Conclusion: Quantifying risk on each of the collected real traffic scenarios makes testing ADFs possible in further more reliable and significant scenarios like near-crashes

Insights in the criticality of bicycle-car and bicycle-truck turning interactions at an urban intersection

Bicyclists represented 14.6% of the road fatalities in Germany in 2019. These accidents hap-pen mostly at urban intersections where bicyclists and motorized road users (MRU) encoun-ter each other. From all the types of MRU that bicycles interact with, trucks represented only 2% of the accidents with bicyclists between 2000 and 2014 in Norway, but up to 20% of road fatalities. Real bicycle-MRU interactions in the context of an urban intersection are studied here to get insights in the behavior of the actors and to identify the sources of risk for bicyclists. For this purpose, new traffic metrics to model criticality (Surrogate Measures of Safety or “SMoSs”) and evasive maneuvers in bicycle-MRU interactions are developed and validated. Scenarios including all four arms of the intersection, different combinations of lanes and also different road user types are considered. In order to gain an understanding of their key characteristics, the bicycle-truck interactions are compared to their bicycle-car equivalents. A recently developed collaborative scenario mining platform was used to automatically identify real traffic scenarios of bicycle-MRU interactions from trajectory data of the AIM Research Intersection in Braunschweig, Germany. This is a large-scale research facility, which records trajectory data with 20 fps with several stereo-cameras at a traffic signal-controlled crossing with bicycle paths. The trajectory data contains information about GNSS-based timestamp, location (UTM), velocity, acceleration, road user type (e.g., pedestrian, bicycle, car) and dimensions of the road users. A total of 196 hours of traffic were analyzed, with interactions occurring most frequently between 6 a.m. and 6 p.m. No interactions were analyzed after 8 p.m. and up to 6 a.m. Three main results are expected from this study of bicycle-MRU interactions. First, novel SMoS that are capable to detect critical and atypical situations as well as evasive maneuvers. Second, observation of patterns in the behavior and origins of conflicts. Third, the main dif-ferences between the bicycle-car and bicycle-truck interactions based on selected parame-ters. A part of the mined scenarios was labelled by human observers to annotate the criticali-ty, atypicality and observed patterns by reviewing the recorded camera data. The post-encroachment time (PET) is a popular SMoS to estimate criticality, but fails in case of interactions where the second road user arriving at the conflict point reacted by braking or swerving (false negative) or if it accelerated in a controlled situation (false positive) to close the gap. On the other hand, the new SMoSs do not incur in these errors because they consider several seconds of the maneuver before the crossing time. One of them was capable to find evasive maneuvers by comparing the projected PET (assuming constant velocities of both actors until the crossing point) during the maneuver with the final PET of the interaction. Bicycle-truck interactions were exceptionally rare in comparison to bicycle-car interactions. The main differences between both types of interactions were examined based on selected dynamic parameters as well as SMoS and the lateral deviation from the driving lane. It ap-peared that trucks deviated more from the driving lane than the cars, but their speeds and PET values were similar. Also, trucks had lower accelerations and decelerations. In conclusion, a better understanding of bicycle-MRU interactions at an urban intersection was obtained, behavioral patterns were detected and models of atypicality and criticality were implemented and validated. This is helpful, for example, for the scenario-based testing and development of automatic driving functions, achieving better traffic simulations and design of infrastructure