Safety-Aware Route Navigation: Driving with Less Sun Glare
Pages 497 - 500
Abstract
Sun glare during driving poses a significant threat to driver and pedestrian safety. Navigation and route planning typically seeks to minimize the distance or time between the desired origin and destination, accounting for traffic patterns and other heuristics like minimizing the number of stoplights or left turns encountered on a route. However, current navigation methods do not support avoidance of complicated, temporally-dependent safety factors, like adverse road and environmental conditions. We take avoiding incident sun glare to the driver as an example of dynamic safety-aware navigation and lay out potential strategies for addressing this previously unexplored problem. We present a reinforcement learning-based method for computing sun glare-low routes through an elastic function that accounts for the direct angle between the sun and the driving direction. Our preliminary work shows that in some cases it is possible to reduce the sun glare exposure on a route by trading off additional travel distance. We envision future safety-aware navigation approaches that can automatically balance this trade-off and account for additional dynamic spatially and temporally-dependent safety-related environmental factors, like road and weather conditions, to determine the safest and most efficient route between any two given points.
References
[1]
M. D Adelfio and H. Samet. 2014. Itinerary retrieval: Travelers, like traveling salesmen, prefer efficient routes. In Proceedings of the 8th Workshop on Geographic Information Retrieval. 1--8.
[2]
M. Z. Arokhlo, A. Selamat, S. Z. M. Hashim, and M. H. Selamat. 2011. Multiagent reinforcement learning for route guidance system. IJACT 3, 6 (2011).
[3]
J. Bennett. 2010. OpenStreetMap. Packt Publishing Ltd.
[4]
G. Boeing. 2017. OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems 65 (2017), 126--139.
[5]
X. Cai, M. Everett, J. Fink, and J. P. How. 2022. Risk-aware off-road navigation via a learned speed distribution map. In 2022 IEEE/RSJ IROS. IEEE, 2931--2937.
[6]
B. Chen and H. H. Cheng. 2010. A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on intelligent transportation systems 11, 2 (2010), 485--497.
[7]
N. Deo and C. Pang. 1984. Shortest-path algorithms: Taxonomy and annotation. Networks 14, 2 (1984), 275--323.
[8]
Y. Geng, E. Liu, R. Wang, Y. Liu, W. Rao, S. Feng, Z. Dong, Z. Fu, and Y. Chen. 2021. Deep Reinforcement Learning Based Dynamic Route Planning for Minimizing Travel Time. In 2021 IEEE ICC Workshops.
[9]
D. Ghosh, J. Sankaranarayanan, K. Khatter, and H. Samet. 2023. In-Path Oracles for Road Networks. ISPRS International Journal of Geo-Information 12, 7 (2023).
[10]
D. Ghosh, J. Sankaranarayanan, K. Khatter, and H. Samet. 2024. Opportunistic package delivery as a service on road networks. Geoinformatica 28, 1 (2024), 53--88.
[11]
Y. Ikeda and M. Inoue. 2016. An evacuation route planning for safety route guidance system after natural disaster using multi-objective genetic algorithm. Procedia computer science 96 (2016), 1323--1331.
[12]
A. Jenkins. 2013. The Sun's position in the sky. European Journal of Physics 34, 3 (2013), 633.
[13]
C. S. Jensen, B. Yang, C. Guo, J. Hu, and K. Torp. 2024. Routing with Massive Trajectory Data. In 2024 IEEE ICDE. IEEE.
[14]
S. Koenig and M. Likhachev. 2005. Fast replanning for navigation in unknown terrain. IEEE Transactions on Robotics 21, 3 (2005), 354--363.
[15]
J. Krumm and E. Horvitz. 2017. Risk-Aware Planning: Methods and Case Study on Safe Driving Route. In Proceedings of AAAI, Vol. 31. 4708--4714.
[16]
X. Li, B. Y. Cai, W. Qiu, J. Zhao, and C. Ratti. 2019. A novel method for predicting and mapping the occurrence of sun glare using Google Street View. Transportation research part C: emerging technologies 106 (2019), 132--144.
[17]
Z. Li, I. Kolmanovsky, E. Atkins, J. Lu, D. P. Filev, and J. Michelini. 2016. Road risk modeling and cloud-aided safety-based route planning. IEEE Transactions on Cybernetics 46, 12 (2016), 3253--3291.
[18]
S. K. M. L. Y. Liu and D. Furcy. [n. d.]. Incremental Heuristic Search in Artificial Intelligence. ([n. d.]).
[19]
S. Mitra. 2014. Sun glare and road safety: An empirical investigation of intersection crashes. Safety science 70 (2014), 246--254.
[20]
S. Opfer, H. Skubch, and K. Geihs. 2011. Cooperative path planning for multi-robot systems in dynamic domains. Mobile Robots-Control Architectures, Bio-Interfacing, Navigation, Multi Robot Motion Planning and Operator Training (2011), 237--258.
[21]
S. A. Pedersen, B. Yang, and C. S. Jensen. 2020. Fast stochastic routing under time-varying uncertainty. The VLDB Journal 29, 4 (2020), 819--839.
[22]
H. Samet. 1983. A quadtree medial axis transform. Commun. ACM 26, 9 (1983), 680--693.
[23]
H. Samet. 1985. Reconstruction of quadtrees from quadtree medial axis transforms. Computer vision, graphics, and image processing 29, 3 (1985), 311--328.
[24]
J. Sankaranarayanan, H. Alborzi, and H. Samet. 2006. Distance join queries on spatial networks. In Proceedings of GIS. 211--218.
[25]
J. Sankaranarayanan and H. Samet. 2009. Distance oracles for spatial networks. In 2009 IEEE ICDE. 652--663.
[26]
J. Sankaranarayanan and H. Samet. 2010. Query processing using distance oracles for spatial networks. IEEE TKDE 22, 8 (2010), 1158--1175.
[27]
J. Sankaranarayanan and H. Samet. 2010. Roads Belong in Databases. Data Engineering (2010), 4.
[28]
A. Stentz. 1997. Optimal and efficient path planning for partially known environments. In Intelligent unmanned ground vehicles. Springer, 203--220.
[29]
S. Triest, M. G. Castro, P. Maheshwari, M. Sivaprakasam, W. Wang, and S. Scherer. 2023. Learning risk-aware costmaps via inverse reinforcement learning for off-road navigation. In 2023 IEEE ICRA. 924--930.
[30]
S. Vasserman, M. Feldman, and A. Hassidim. 2015. Implementing the Wisdom of Waze.
[31]
C. J. C. H. Watkins. 1989. Learning from delayed rewards. (1989).
[32]
C. J. C. H. Watkins and P. Dayan. 1992. Q-learning. Machine learning 8 (1992), 279--292.
[33]
M. Zolfpour-Arokhlo, A. Selamat, S. Z. M. Hashim, and H. Afkhami. 2014. Modeling of route planning system based on Q value-based dynamic programming with multi-agent reinforcement learning algorithms. Engineering Applications of Artificial Intelligence 29 (2014), 163--177.
Index Terms
- Safety-Aware Route Navigation: Driving with Less Sun Glare
Recommendations
Time-optimal and privacy preserving route planning for carpool policy
AbstractTo alleviate the traffic congestion caused by the sharp increase in the number of private cars and save commuting costs, taxi carpooling service has become the choice of many people. Current research on taxi carpooling services has focused on ...
Comments
Information & Contributors
Information
Published In
October 2024
743 pages
ISBN:9798400711077
DOI:10.1145/3678717
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 November 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
Conference
SIGSPATIAL '24
Sponsor:
SIGSPATIAL '24: The 32nd ACM International Conference on Advances in Geographic Information Systems
October 29 - November 1, 2024
GA, Atlanta, USA
Acceptance Rates
SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
Overall Acceptance Rate 257 of 1,238 submissions, 21%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 33Total Downloads
- Downloads (Last 12 months)33
- Downloads (Last 6 weeks)17
Reflects downloads up to 14 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in