Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets

By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen in the next few moments and consider these foresights in their driving behavior. To make the driving style of automated vehicles comparable to the one of human drivers with respect to comfort and perceived safety, the aforementioned anticipation skills need to become a built-in feature of self-driving vehicles. This article provides a systematic comparison of methods and strategies to generate this intention for self-driving cars using machine learning techniques. To implement and test these algorithms we use a large data set collected over more than 30000 km of highway driving and containing approximately 40000 real-world driving situations. We further show that it is possible to classify driving maneuvers upcoming within the next 5 s with an Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes. This enables us to predict the lateral position with a prediction horizon of 5 s with a median lateral error of less than 0.21 m.Comment: the paper has been accepted for publication in IEEE Transcations on Intelligent Transportation Systems (T-ITS) 16 pages 13 figures 12 table

Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0.18m on a prediction horizon of 5s.Comment: the article has been accepted for publication during the 23rd IEEE Intelligent Transportation Systems Conference (ITSC), 7 pages, 6 figures, 1 tabl

A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions

Already today, driver assistance systems help to make daily traffic more comfortable and safer. However, there are still situations that are quite rare but are hard to handle at the same time. In order to cope with these situations and to bridge the gap towards fully automated driving, it becomes necessary to not only collect enormous amounts of data but rather the right ones. This data can be used to develop and validate the systems through machine learning and simulation pipelines. Along this line this paper presents a fleet learning-based architecture that enables continuous improvements of systems predicting the movement of surrounding traffic participants. Moreover, the presented architecture is applied to a testing vehicle in order to prove the fundamental feasibility of the system. Finally, it is shown that the system collects meaningful data which are helpful to improve the underlying prediction systems.Comment: the article has been accepted for publication during the 2020 IEEE Symposium Series on Computational Intelligence (SSCI) within the IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 7 pages, 6 figure

Learning viewpoint invariant object representations using a temporal coherence principle

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the "stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells' input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an - also unlabeled - distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformation

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks

To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this temporal information might be even more useful. This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways using long short-term memory-based recurrent neural networks. An extensive evaluation based on a large real-world data set shows that our approach is able to make reliable predictions, even in the most challenging situations, with a root mean squared error around 0.7 seconds. Already 3.5 seconds prior to lane changes the predictions become highly accurate, showing a median error of less than 0.25 seconds. In summary, this article forms a fundamental step towards downstreamed highly accurate position predictions.Comment: the article has been accepted for publication in IEEE Robotics and Automation Letters (RA-L); the article has been submitted to RA-L with IEEE ICRA conference option; if the article will be presented during the conference will be decided independently; 8 pages, 5 figures, 6 table

Learning viewpoint invariant object representations using a temporal coherence principle

Invariant object recognition is arguably one of the major challenges for contemporary machine vision systems. In contrast, the mammalian visual system performs this task virtually effortlessly. How can we exploit our knowledge on the biological system to improve artificial systems? Our understanding of the mammalian early visual system has been augmented by the discovery that general coding principles could explain many aspects of neuronal response properties. How can such schemes be transferred to system level performance? In the present study we train cells on a particular variant of the general principle of temporal coherence, the “stability” objective. These cells are trained on unlabeled real-world images without a teaching signal. We show that after training, the cells form a representation that is largely independent of the viewpoint from which the stimulus is looked at. This finding includes generalization to previously unseen viewpoints. The achieved representation is better suited for view-point invariant object classification than the cells’ input patterns. This property to facilitate view-point invariant classification is maintained even if training and classification take place in the presence of an – also unlabeled – distractor object. In summary, here we show that unsupervised learning using a general coding principle facilitates the classification of real-world objects, that are not segmented from the background and undergo complex, non-isomorphic, transformations

A tale of worldwide success: Behind the scenes of Carex (Cyperaceae) biogeography and diversification

The megadiverse genus Carex (c. 2000 species, Cyperaceae) has a nearly cosmopolitan distribution, displaying an inverted latitudinal richness gradient with higher species diversity in cold-temperate areas of the Northern Hemisphere. Despite great expansion in our knowledge of the phylogenetic history of the genus and many molecular studies focusing on the biogeography of particular groups during the last few decades, a global analysis of Carex biogeography and diversification is still lacking. For this purpose, we built the hitherto most comprehensive Carex-dated phylogeny based on three markers (ETS–ITS–matK), using a previous phylogenomic Hyb-Seq framework, and a sampling of two-thirds of its species and all recognized sections. Ancestral area reconstruction, biogeographic stochastic mapping, and diversification rate analyses were conducted to elucidate macroevolutionary biogeographic and diversification patterns. Our results reveal that Carex originated in the late Eocene in E Asia, where it probably remained until the synchronous diversification of its main subgeneric lineages during the late Oligocene. E Asia is supported as the cradle of Carex diversification, as well as a “museum” of extant species diversity. Subsequent “out-of-Asia” colonization patterns feature multiple asymmetric dispersals clustered toward present times among the Northern Hemisphere regions, with major regions acting both as source and sink (especially Asia and North America), as well as several independent colonization events of the Southern Hemisphere. We detected 13 notable diversification rate shifts during the last 10 My, including remarkable radiations in North America and New Zealand, which occurred concurrently with the late Neogene global cooling, which suggests that diversification involved the colonization of new areas and expansion into novel areas of niche space.This work was carried out with financial support by the National Science Foundation (Award #1255901 to ALH and Award #1256033 to EHR), the Spanish Ministry of Economy and Competitiveness (project CGL2016–77401‐P to SM-B and ML), the USDA National Institute of Food and Agriculture (McIntire Stennis project 1018692 to DS) as well as postdoctoral fellowships towards SM‐B (Universidad Pablo de Olavide, PP16/12‐APP), and PJ‐M (National Science Foundation, Award #1256033, and the Smithsonian Postdoctoral Fellowship program)

A new classification of Cyperaceae (Poales) supported by phylogenomic data

Cyperaceae (sedges) are the third largest monocot family and are of considerable economic and ecological importance. Sedges represent an ideal model family to study evolutionary biology because of their species richness, global distribution, large discrepancies in lineage diversity, broad range of ecological preferences, and adaptations including multiple origins of C4 photosynthesis and holocentric chromosomes. Goetghebeur’s seminal work on Cyperaceae published in 1998 provided the most recent complete classification at tribal and generic level, based on a morphological study of Cyperaceae inflorescence, spikelet, flower and embryo characters plus anatomical and other information. Since then, several family‐level molecular phylogenetic studies using Sanger sequence data have been published. Here, more than 20 years after the last comprehensive classification of the family, we present the first family‐wide phylogenomic study of Cyperaceae based on targeted sequencing using the Angiosperms353 probe kit sampling 311 accessions. Additionally, 62 accessions available from GenBank were mined for overlapping reads and included in the phylogenomic analyses. Informed by this backbone phylogeny, a new classification for the family at the tribal, subtribal and generic levels is proposed. The majority of previously recognized suprageneric groups are supported, and for the first time we establish support for tribe Cryptangieae as a clade including the genus Koyamaea. We provide a taxonomic treatment including identification keys and diagnoses for the 2 subfamilies, 24 tribes and 10 subtribes and basic information on the 95 genera. The classification includes five new subtribes in tribe Schoeneae: Anthelepidinae, Caustiinae, Gymnoschoeninae, Lepidospermatinae and Oreobolinae. This article is protected by copyright. All rights reserved