ABSTRACT
In the recent years, improvements in vehicular technology has been significant. Even after this improvement, right now it is only a fraction of what is being expected in the future. Vehicles in the future will be able to sense its environment and navigate the surroundings without any sort of human input. These vehicles are introduced as Connected and Autonomous Vehicles. These data can be used to develop different applications that can enhance the road safety, better manage the traffic flow and provide additional comfort services to the vehicle drivers. To do so, autonomous vehicles need to have accurate and real time localization estimation. Obviously, when talking about the vehicle position the Global positioning System (GPS) is the first possibility that comes to mind. However, the GPS system shows that it cannot keep the same evolution speed as the vehicles. This paper evaluates the state-of-the-art vehicle localization techniques and investigates their applicability on autonomous vehicles. Each of the localization techniques has cons and pros and cannot work alone.
- Parkin, J., Clark, B., Clayton, W.; Ricci, M., Parkhurst, G 2018. Autonomous vehicle interactions in the urban street environment: A research agenda. Proc. Inst. Civ. Eng. Munic. Eng. 2018, 171, pp. 15--25.Google Scholar
- Igliński, H.; Babiak, M. 2017. Analysis of the Potential of Autonomous Vehicles in Reducing the Emissions of Greenhouse Gases in Road Transport. Procedia Engineering, volume 192, pp. 353--358, ISSN 1877-7058.Google ScholarCross Ref
- Duarte, F., Ratti, C. 2018. The impact of autonomous vehicles on cities: A review". Journal of Urban Technology, 25(4), 3--18.Google ScholarCross Ref
- D. A. Pomerleau, 1989. Alvinn: An autonomous land vehicle in a neural network. In Advances in neural information processing systems, 1989, pp. 305--313.Google Scholar
- U. Muller et al., 2006. Off-road obstacle avoidance through end-to-end learning. in Advances in neural information processing systems, pp. 739--746.Google Scholar
- M. Bojarski et al., 2016. End to end learning for self-driving cars, 1604.07316, 2016.Google Scholar
- D. B. Rawat, G. Yan,, D. Popescu,, M. Weigle,, S. Olariu, 2009. Dynamic Adaptation of Joint Transmission Power and Contention Window in VANET. IEEE Vehicular Technology Conference - Fall'09.Google Scholar
- B. Parno,, & A. Perrig, 2005. Challenges in securing vehicular networks. Fourth Workshop on Hot Topics in Networks (HotNets-IV).Google Scholar
- Tseng, Y, Ni, C, Chen, S. -Y, & Sheu, Y. -S. J.-P. 2002. The Broadcast Storm Problem in a Mobile ad hoc Network. Wireless Networks, Vol. 8. Pp. 153--167.Google Scholar
- http://bbcr.uwaterloo.ca/SubGroup/securitybbcr/vanet.html. [Online] Accessed: 20 june 2019.Google Scholar
- H. Cho, 2014. Cooperative Intersection Collision -Warning System Based on Vehicle-to-Vehicle Communication, Contemporary Engineering Sciences, vol. 7, no. 22, 1147 -- 1154.Google ScholarCross Ref
- K.N. Qureshi, A.H. Abdullah, 2014. Localization-Based System Challenges in Vehicular Ad Hoc Networks: Survey. Smart Comput. Rev., 4 (6), pp. 515--528.Google Scholar
- A. El-Rabbany, 2006. Introduction to GPS: The Global Positioning System. Boston, MA: Artech House, second edition.Google Scholar
- Pink, O. Hummel, B. 2008. A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In the 11th International IEEE Conference on Intelligent Transportation Systems.Google ScholarCross Ref
- M. Chen, D. Haehnel, J. Hightower, T. Sohn, A. LaMarca, I. Smith, D. Chmelev, J. Hughes, F. Potter, 2006. Practical metropolitan-scale positioning for GSM phones. Proceedings of 8th Ubicomp, Orange County, California, pp. 225--242.Google ScholarDigital Library
- T. Alhmiedat, G. Samara, and A. O. A. Salem, "An indoor fingerprinting localization approach for ZigBee wireless sensor networks," European Journal of Scientific Research, vol. 105, 2013, pp. 190--202.Google Scholar
- Yang, H., Shao, L., Zheng, F., Wang, L., Song, Z. 2011. Recent advances and trends in visual tracking: A review. Neurocomputing, 74(18), pp. 3823--3831.Google ScholarDigital Library
- R. Parker, S. Valaee, 2006. Vehicle localization in Vehicular Networks. In Vehicular Technology Conference, 2006. VTC2006.Google ScholarCross Ref
- Litman, T., Autonomous Vehicle Implementation Predictions. Victoria Transport Policy Institute. 2019, http://www.vtpi.org/avip.pdfGoogle Scholar
Recommendations
A Highway Toll Lane Framework that Unites Autonomous Vehicles and High-occupancy Vehicles
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)We consider the scenario where human-driven/autonomous vehicles with low/high occupancy are sharing a segment of highway and autonomous vehicles are capable of increasing the traffic throughput by preserving a shorter headway than human-driven vehicles. ...
Autonomous Vehicles Empty Cruising Impact on Parking Dynamics
IWCTS '23: Proceedings of the 16th ACM SIGSPATIAL International Workshop on Computational Transportation ScienceAs development and utilization of autonomous vehicles is gaining momentum, it is expected to be decades of mixed environment where autonomous and human-driven vehicles will co-exist. Autonomous vehicles' ability to perform empty cruising can impact the ...
A software framework of roadside units for traffic condition perception and broadcast
RACS '22: Proceedings of the Conference on Research in Adaptive and Convergent SystemsWhile vehicle-to-everything technology has been proposed to improve road traffic efficiency and safety, it would suffer from the low coverage during an early stage of vehicle-to-everything deployment. The infrastructure-based solutions have been ...
Comments