Learn from IoT: Pedestrian Detection and Intention Prediction for Autonomous Driving Gürkan Solmaz, Everton Luis Berz, Marzieh Farahani Dolatabadi*, Samet Aytac, Jonathan Fürst, Bin Cheng, Jos den Ouden* IoT Research Group NEC Laboratories Europe * Eindhoven University of Technology (TU/e) 1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability (SMAS), ACM MobiCom 2019 Los Cabos, Mexico, October 21, 2019 This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the project AUTOPILOT (Automated Driving Progressed by Internet Of Things) under the grant agreement No 731993. Responsibility for the information and views set out in this document lies entirely with the authors.
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Learn from IoT: Pedestrian Detection and Intention Prediction for Autonomous Driving
Gürkan Solmaz, Everton Luis Berz, Marzieh Farahani Dolatabadi*, Samet Aytac, Jonathan Fürst, Bin Cheng, Jos den Ouden*
IoT Research Group
NEC Laboratories Europe
* Eindhoven University of Technology (TU/e)
1st ACM Workshop on Emerging Smart Technologies and Infrastructures for Smart Mobility and Sustainability (SMAS), ACM MobiCom 2019
Los Cabos, Mexico, October 21, 2019
This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the project AUTOPILOT (Automated Driving Progressed by Internet Of Things) under the grant agreement No 731993. Responsibility for the information and views set out in this document lies entirely with the authors.
▌WM contains the vehicle itself and objects around
▌The formalism adopted is WIRE where the
WM aims to track semantic objects such as VRUs http://wiki.ros.org/wire
(World Information for Robot Environments framework by TU/e)
▌Multiple Hypothesis Tracker (MHT) algorithm [2] combines evidences to a common world representation dynamically
▌Objects’ attributes, classification, and prior knowledge are associated in the hypotheses treeEvery hypothesis contains a list of anchors and has a correctness probability
Each anchor contains an individual symbol, a set of measurements and a probabilistic signature that consists out of a mixture of probability density functions generated by a set of behavior models
The predicate attribute space represents predicate grounding relations that link attribute values and predicate symbols [2]
▌Smartphone to vehicle delay ~0.6secFrom smartphone to cloud and lastly in-vehicle IoT platform (ROS timestamps)
▌In the WM, vehicle and mobile devices together has more consistency compared to only vehicleVehicle receives the global location of a pedestrian a few seconds before the
This work has received funding from the European Union’s Horizon 2020 research and innovation programme within the project AUTOPILOT (Automated Driving Progressed by Internet Of Things) under the grant agreement No 731993. Responsibility for the information and views set out in this document lies entirely with the authors.