Is the Pedestrian going to Cross? Answering by 2D Pose Estimation

Our recent work suggests that, thanks to nowadays powerful CNNs, image-based 2D pose estimation is a promising cue for determining pedestrian intentions such as crossing the road in the path of the ego-vehicle, stopping before entering the road, and starting to walk or bending towards the road. This statement is based on the results obtained on non-naturalistic sequences (Daimler dataset), i.e. in sequences choreographed specifically for performing the study. Fortunately, a new publicly available dataset (JAAD) has appeared recently to allow developing methods for detecting pedestrian intentions in naturalistic driving conditions; more specifically, for addressing the relevant question is the pedestrian going to cross? Accordingly, in this paper we use JAAD to assess the usefulness of 2D pose estimation for answering such a question. We combine CNN-based pedestrian detection, tracking and pose estimation to predict the crossing action from monocular images. Overall, the proposed pipeline provides new state-of-the-art results.Comment: This is a paper presented in IEEE Intelligent Vehicles Symposium (IEEE IV 2018

Analysis and Prediction of Pedestrian Crosswalk Behavior during Automated Vehicle Interactions

For safe navigation around pedestrians, automated vehicles (AVs) need to plan their motion by accurately predicting pedestrians’ trajectories over long time horizons. Current approaches to AV motion planning around crosswalks predict only for short time horizons (1-2 s) and are based on data from pedestrian interactions with human-driven vehicles (HDVs). In this paper, we develop a hybrid systems model that uses pedestrians’ gap acceptance behavior and constant velocity dynamics for long-term pedestrian trajectory prediction when interacting with AVs. Results demonstrate the applicability of the model for long-term (> 5 s) pedestrian trajectory prediction at crosswalks. Further, we compared measures of pedestrian crossing behaviors in the immersive virtual environment (when interacting with AVs) to that in the real world (results of published studies of pedestrians interacting with HDVs), and found similarities between the two. These similarities demonstrate the applicability of the hybrid model of AV interactions developed from an immersive virtual environment (IVE) for real-world scenarios for both AVs and HDVs.Toyota Research Institute (TRI) provided funds to assist the authors with their research, but this article solely reflects the opinions and conclusions of its authors and not TRI or any other Toyota entity. The work was also supported in part by the National Science Foundation and supported in part by the Automotive Research Center at the University of Michigan, with funding from government contract Department of the Army W56HZV- 14-2-0001 through the U.S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC).Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154053/1/ICRA_2020_Analysis_and_Prediction_of_Pedestrian_Final_revised_03_03_20.pdfDescription of ICRA_2020_Analysis_and_Prediction_of_Pedestrian_Final_revised_03_03_20.pdf : Main fil

SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving

To mitigate the challenges arising from partial occlusion in human pose keypoint based pedestrian detection methods , we present a novel pedestrian pose keypoint completion method called the separation and dimensionality reduction-based generative adversarial imputation networks (SDR-GAIN) . Firstly, we utilize OpenPose to estimate pedestrian poses in images. Then, we isolate the head and torso keypoints of pedestrians with incomplete keypoints due to occlusion or other factors and perform dimensionality reduction to enhance features and further unify feature distribution. Finally, we introduce two generative models based on the generative adversarial networks (GAN) framework, which incorporate Huber loss, residual structure, and L1 regularization to generate missing parts of the incomplete head and torso pose keypoints of partially occluded pedestrians, resulting in pose completion. Our experiments on MS COCO and JAAD datasets demonstrate that SDR-GAIN outperforms basic GAIN framework, interpolation methods PCHIP and MAkima, machine learning methods k-NN and MissForest in terms of pose completion task. In addition, the runtime of SDR-GAIN is approximately 0.4ms, displaying high real-time performance and significant application value in the field of autonomous driving

Implicaciones éticas de los vehículos de conducción autónoma

Los vehículos de conducción autónoma traen consigo un conjunto de implicaciones de carácter ético que deben ser tomadas en consideración antes de su adopción en nuestras vías. Más allá de la capa más mediática, compuesta por los dilemas en situaciones de accidente, en este artículo se presentan algunas de las implicaciones con más peso de cara al futuro cercano, organizadas en base a un conjunto de ámbitos concretos, tales como la sociedad, la economía, el medio ambiente y la ética y viabilidad del software.Autonomous driving vehicles bring with them a pack of ethical implications that should be taken in consideration before their adoption on our roads. Starting from the most media part, traffic accident dilemmas, this article holds some of the major implications based on a near future, grouped in some concrete areas, such as society, economy, environmental consequences and the software ethics and viability.Els vehicles de conducció autònoma comporten un conjunt d'implicacions de caràcter ètic que s'han de tenir en compte abans de la seva adopció a les nostres vies. Més enllà de la capa més mediàtica, composta pels dilemes en situacions d'accidents, en aquest article es tracten algunes de les implicacions de més pes de cara al futur pròxim, organitzades d'acord amb un conjunt d'àmbits concrets, tals com la societat, l'economia, el medi ambient, i l'ètica i viabilitat del software

A Study on Human Pose Data Anomaly Detection

Identifying anomalous human pose data is crucial to many emerging data-driven artificial intelligence systems. For instance, patient behavior monitoring systems can analyze patient behavior based on patient movement and pose predictions. Although pose tracking methods have improved over the years, anomalous pose estimates, even if infrequent, can result in troublesome events, such as error information on the patient behaviors, which can lead to false diagnosis and requires human labor intensive processes to identify those anomalous poses. This cost could be mitigated by correcting or identifying anomalous pose estimates in an automated fashion. Thus, we present an anomaly analysis framework for clinical human pose estimates to address these concerns.In this study, we define anomalous human pose estimates by a thresholded euclidean distance between manually labeled joints and computer vision based predictions of joint locations. For our study, we annotated and analyzed a new human pose dataset from a clinical setting to study the subject-wise sensitivity and accuracy of anomaly detection on our proposed variational autoencoder (VAEs) } based frameworks. For our study, we performed anomaly analysis and detection based on our frameworks with PatientPose , a 2D pose estimator designed for the clinic setting. We demonstrate a strategy to correct anomalous to improve pose estimation accuracy and quantify and consider design-tradeoffs for our anomalous pose detection method. We also compare our method with classic anomaly detection methods such as Isolation Forest and One-Class Support Vector Machine (OC-SVM) with time-domain input. The outcome of this study will provide an out-of-the-box anomaly detection methods for clinical human pose data estimation frameworks and empower follow up research and systems development with imperfect human pose data