The Atlas of Lane Changes: Investigating Location-Dependent Lane Change Behaviors Using Measurement Data from a Customer Fleet
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The Atlas of Lane Changes: Investigating Location-dependent Lane Change Behaviors Using Measurement Data from a Customer Fleet Florian Wirthmuller¨ , Jochen Hipp , Christian Reichenbacher¨ and Manfred Reichert Abstract— The prediction of surrounding traffic participants behavior is a crucial and challenging task for driver assistance and autonomous driving systems. Today’s approaches mainly focus on modeling dynamic aspects of the traffic situation and try to predict traffic participants behavior based on this. In this article we take a first step towards extending this common practice by calculating location-specific a-priori lane change probabilities. The idea behind this is straight forward: The driving behavior of humans may vary in exactly the same traffic situation depending on the respective location. E. g. drivers may ask themselves: Should I pass the truck in front of me immediately or should I wait until reaching the less curvy part of my route lying only a few kilometers ahead? Although, such information is far away from allowing behavior prediction on its own, it is obvious that today’s approaches will greatly benefit when incorporating such location-specific a-priori probabilities into their predictions. For example, our investigations show that highway interchanges tend to enhance driver’s motivation to perform lane changes, whereas curves seem to have lane change-dampening effects. Nevertheless, the investigation of all considered local conditions shows that superposition of various effects can lead to unexpected probabilities at some locations. We thus suggest dynamically constructing and maintaining a lane change probability map based on customer fleet data in order to support onboard prediction systems with additional information. For deriving reliable lane change probabilities a broad customer fleet is the key to success. Fig. 1. Heatmap of lane changes detected in the collected measurements I. INTRODUCTION on German highways. Basemap: OpenStreetMap [1] In general, experienced drivers are aware that they behave differently when facing the same traffic situation depending on the respective location. In this context, various local In order to navigate safely and comfortably for the pas- conditions, as e. g. the curviness, the width of the road and sengers through any traffic situation automated vehicles need the roadside structures or the placement of the signage, play to predict surrounding vehicle’s behaviors. Although this is an important role. already a challenging task, the above described effect makes arXiv:2107.04029v2 [cs.CY] 9 Jul 2021 it even more challenging. In order to support such predic- F. Wirthmuller,¨ J. Hipp and C. Reichenbacher¨ are with Mercedes-Benz AG, Boblingen,¨ Germany, E-Mail: ffirst name.last [email protected]. tions, this article aims to analyze which local conditions F. Wirthmuller¨ and M. Reichert are with the Institute of Databases and are the most important and to which extent they influence Information Systems (DBIS) at Ulm University, Ulm, Germany, the driving behavior. In our research, we focus on the lane E-Mail: ffirst name.last [email protected] C. Reichenbacher¨ is with the Wilhelm-Schickard-Institute for Informatics change behavior in highway scenarios. at Eberhard Karls University Tubingen,¨ Tubingen,¨ Germany. For our analyses, we collect measurement data from a fleet ORCID (ordered as authors above): https://orcid.org/0000-0002-9732-2561; of customer vehicles. Note that most automotive software https://orcid.org/0000-0002-9037-9899; functions are developed and tested based on data collected https://orcid.org/0000-0002-0907-3287; with dedicated test vehicles, so far. This, however, results https://orcid.org/0000-0003-2536-4153 © 2021 IEEE. Personal use of this material is permitted. Permission from in several limitations. For instance, test drivers, knowing IEEE must be obtained for all other uses, in any current or future media, their route very well tend to drive in a special manner. including reprinting/republishing this material for advertising or promotional Data collected from real customers therefore might be more purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in representative. Besides, considering customer data offers the other works. chance to cover much more miles in less time. © 2021 IEEE After collecting the data, they are processed and spatially area. While doing so, problems in terms of storage capacity aggregated using digital maps. As a result we obtain a map of as well as computation time could arise. In addition, pure lane change probabilities. Together with other map attributes online prediction techniques are suffering from transmission this enables downstreamed analyses. times. In detail, we make the following contributions: [7] uses the curvature of the road as input to plan com- 1) A procedure to construct a lane change probability map fortable trajectories. The plain fact that location-dependent 2) A thorough study and discussion of the impact of features are also included while planing pleasant trajectories three exemplary location-dependent conditions (inter- additionally emphasizes that experienced drivers also adapt changes, curvature, slope) on the lane change behavior to changed local conditions. The remainder of this article is structured as follows: [8] studies location-dependent lane change behaviors on Sec. II discusses related work. Sec. III outlines the prepara- arterial roads from the viewpoint of traffic flow analytics. To tion of the map and measurement data. Subsequently, Sec. IV do so, the authors solve the motion prediction task through presents our empiric investigations and discusses the results classical modelling. The established model distinguishes obtained. Finally, Sec. V summarizes our contribution and between efficiency-driven and objective-driven lane changes provides an outlook on future work. and aggregates the contributions of both motivations. The efficiency-driven model part on the one hand is primarily II. RELATED WORK based on the motion of surrounding vehicles. The objective- A general overview on motion prediction approaches and driven part on the other hand mostly relies on the location. especially deep learning-based ones is given in [2], [3]. Be- To correctly parametrize the model, the authors use the sides characterizing and categorizing approaches, [3] shows well-studied NGSIM data set [9]. Here, the term location- typical strengths and weaknesses of each group. Among dependent has to be comprehended as microscopic rather other things, it is mentioned that environment conditions, than macroscopic. The location-dependent nature of the such as, road geometry and traffic rules can have significant approach solely refers to the location within the road segment impacts on the driving behavior. Most shown approaches are, observed in the NGSIM data set, which basically describes however, not able to handle such impacts due to their input in which lane the vehicle is driving and how far away the representation. For the latter, most approaches solely rely on next intersection is. Macroscopic effects such as differences the motion history of the vehicle to be predicted as well as its between regions or cities that our article focuses on are not surrounding vehicles history. Other approaches building up taken into account. on raw sensor data or birds eye views are on the other hand The article presented in [10] originates from a completely computationally expensive. This makes such approaches hard different community being interested in so-called concept to implement on onboard hardware with limited resources. drifts. Concept drifts in general denote changes in classifica- [4] agrees with [3] about the importance of external tion problems over lifetime. The latter, can make previously conditions for the driving behavior. The authors explicitly well-working classifiers inadequate. [10] deals with the de- mention weather, daytime and traffic density. Subsequently, velopment of a concept drift detector that in particular is the latter’s influence on the prediction capability of a known able to detect changes in the a-priori probabilities of the motion prediction system rather than on the driving behavior underlying classes. The general idea of a concept drift, even itself is studied. The article confirms the influence of varying if, spatially than temporally, can also be translated to our traffic densities and concludes that such impacts need to be maneuver classification problem. studied more in detail and integrated in motion prediction [11] also tackles concept drifts in imbalanced online learn- approaches. ing applications. The approach dynamically detects changes [5] describes the development of an architecture concept in the class probabilities and adapts its online learner in order aiming to enhance behavior predictions through massive to reflect that. online learning using customer fleets. The general idea here Concluding our literature study, there exists to the best is to learn several context specific prediction models and of our knowledge no research work that spatially aggregates provide them to all vehicles. lane change probabilities. This fact may be attributed to the [6] is also tackling the motion prediction problem in enormous efforts, being necessary to collect the underlying diverse environments. In order to that, all vehicles of the fleet measurement data. Consequently, analyses about location- are permanently connected to a cloud