Abstract
Many drives in crowded cities end with a challenging parking search, and visitors often do not know which streets allow on-street parking. Therefore, we present a learning-based approach to automatically generate on-street parking maps from parked vehicle positions detected by sensing vehicles. Multiple sets of features are proposed to describe the occupancy of every small road segment and its surroundings at different time instances. The usage of k-means algorithm as unsupervised learning and random forests as supervised learning are compared by applying these feature sets. The proposed approach is evaluated with repeated LiDAR measurements on more than five kilometers of potential parking space length. Our approaches, while keeping the model more generic, reveal slightly better results than an approach from literature. In particular, the unsupervised approach does not need a training data set and is free of any area specific parameter choice.
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References
Bengler K, Dietmayer K, Farber B, Maurer M, Stiller C, Winner H (2014) Three decades of driver assistance systems: review and future perspectives. IEEE Intell Transp Syst Mag 6(4):6–22
Bock F, Eggert D, Sester M (2015) On-street parking statistics using lidar mobile mapping. In: 2015 IEEE 18th international conference on intelligent transportation systems. IEEE, pp 2812–2818
Breiman L (2001) Random forests. Machine learning, pp 5–32
Choi J, Chang E, Yoon D, Ryu S, Jung H, Suhr J (2014) Sensor fusion-based parking assist system. Technical Representative, SAE Technical Paper
Coric V, Gruteser M (2013) Crowdsensing maps of on-street parking spaces. In: 2013 IEEE International conference on distributed computing in sensor systems. IEEE, pp 115–122
Davies J, Beresford A, Hopper A (2006) Scalable, distributed, real-time map generation. IEEE Pervasive Comput 5(4):47–54
Furgale P, Schwesinger U, Rufli M, Derendarz W, Grimmett H, Muhlfellner P, Wonneberger S, Timpner J, Rottmann S, Li B, Schmidt B, Nguyen TN, Cardarelli E, Cattani S, Bruning S, Horstmann S, Stellmacher M, Mielenz H, Koser K, Beermann M, Hane C, Heng L, Lee GH, Fraundorfer F, Iser R, Triebel R, Posner I, Newman P, Wolf L, Pollefeys M, Brosig S, Effertz J, Pradalier C, Siegwart R (2013) Toward automated driving in cities using close-to-market sensors: an overview of the v-charge project. 2013 IEEE intelligent vehicles symposium (IV). IEEE, Iv, pp 809–816
Ge Y, Xue W, Shu Z (2013) Improved system for parknet mobile network. Proceedings of the FISITA 2012 world automotive congress SE—11. Lecture notes in electrical engineering, vol 200. Springer, Berlin, pp 131–145
Mathur S, Jin T, Kasturirangan N, Chandrashekharan J, Xue W, Gruteser M, Trappe W (2010) Parknet: drive-by sensing of road-side parking statistics. In: Proceedings of the 8th international conference on mobile systems, applications, and services—MobiSys ’10
Park Wj, Kim Bs, Seo De, Kim Ds, Lee Kh (2008) Parking space detection using ultrasonic sensor in parking assistance system. In: 2008 IEEE intelligent vehicles symposium, pp 1039–1044
SFMTA (2014) SFpark Putting Theory Into Practice
Shoup DC (2006) Cruising for parking. Transp Policy 13(6):479–486
Shoup DC (2007) Cruising for parking. Access 30:16–22
Stenneth L, Wolfson O, Xu B, Yu PS (2012) Phonepark: street parking using mobile phones. In: 2012 IEEE 13th international conference on mobile data management, pp 278–279
Thornton DA, Redmill K, Coifman B (2014) Automated parking surveys from a LIDAR equipped vehicle. Transportation research part C: emerging technologies 39:23–35
Thrun S (2002) Robotic mapping: a survey. In: Nebel B, Lakemeyer G (eds) Morgan kaufmann, exploring artificial intelligence in the new millenium
Van Ommeren JN, Wentink D, Rietveld P (2012) Empirical evidence on cruising for parking. Transportation research part A: policy and practice 46(1):123–130
White P (2007) No vacancy: park slope’s parking problem and how to fix it. Transaltorg
Acknowledgments
This research has been supported by the German Research Foundation (DFG) through the Research Training Group SocialCars (GRK 1931). The focus of the SocialCars Research Training Group is on significantly improving the city’s future road traffic, through cooperative approaches. This support is gratefully acknowledged.
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Bock, F., Liu, J., Sester, M. (2016). Learning On-Street Parking Maps from Position Information of Parked Vehicles. In: Sarjakoski, T., Santos, M., Sarjakoski, L. (eds) Geospatial Data in a Changing World. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-33783-8_17
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DOI: https://doi.org/10.1007/978-3-319-33783-8_17
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