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ally straightforward and does not require complex
decision-making situations like intersection handling,
pedestrian interventions, distorted roads, and so on.
The most important aspects of highway driving are
longitudinal control to keep a safe distance from other
objects, and lateral control to make lane changes
when it is necessary. Therefore, one of the required
aspects of highway autonomous driving is evaluating
the traffic condition to decide whether a lane change
is needed and does it suit safety and comfort criteria.
In this regard, we design a two-step model that in-
cludes a probabilistic assessment of road lanes and a
deterministic assessment of the inter-vehicular gaps.
For the probabilistic assessment, utility functions that
are combinations of road factors are used. Highway
lanes are constantly evaluated concerning the utility
functions. Then, if a lane change is desired, the gap
selection algorithm starts evaluating inter-vehicular
gaps in the lanes to perform a safe and comfortable
lane change.
In addition to the virtual tests performed with the
sensor data collected during driving on highway, our
model was also integrated into a vehicle and tested
in real world conditions. The vehicle tests on the
highway drive indicate that our model is capable of
assessing the road conditions and reacting to the en-
vironmental changes conservatively. The test vehi-
cle avoided any dangerous maneuvers while driving,
and it generally tended to continue in the lane it was
in. Nevertheless, one limitation of our model is the
lack of assessing the future states of the environment
with a robust prediction model. Instead, we utilize a
constant-acceleration model that does not assume any
lateral maneuvers.
ACKNOWLEDGMENT
This work is supported by the Scientific and Techno-
logical Research Council of Turkey (TUBITAK) un-
der Grant No. 5169901.
REFERENCES
Ahmed, K. I. (1999). Modeling Drivers’ Acceleration and
Lane Changing Behavior. PhD thesis, MIT.
Alizadeh, A., Moghadam, M., Bicer, Y., Ure, N. K., Yavas,
U., and Kurtulus, C. (2019). Automated lane change
decision making using deep reinforcement learning in
dynamic and uncertain highway environment. In 2019
IEEE Intelligent Transportation Systems Conference
(ITSC), pages 1399–1404.
Ardelt, M., Coester, C., and Kaempchen, N. (2012). Highly
automated driving on freeways in real traffic using a
probabilistic framework. IEEE Transactions on Intel-
ligent Transportation Systems, 13(4):1576–1585.
Brechtel, S. (2015). Dynamic Decision-making in Contin-
uous Partially Observable Domains: A Novel Method
and its Application for Autonomous Driving. PhD the-
sis, Karlsruher Instituts f
¨
ur Technologie (KIT).
Brechtel, S., Gindele, T., and Dillmann, R. (2011). Prob-
abilistic MDP-Behavior Planning for Cars. In 2011
IEEE 14th International Conference on Intelligent
Transportation Systems, pages 1537–1542, Washing-
ton.
Brechtel, S., Gindele, T., and Dillmann, R. (2014). Prob-
abilistic Decision-Making under Uncertainty for Au-
tonomous Driving using Continuous POMDPs. In
17th International IEEE Conference on Intelligent
Transportation Systems (ITSC), pages 392–399, Qing-
dao.
EGM, T. (2020). Trafik
˙
Istatistik b
¨
ulteni Traffic statistics
bulletin].
Gipps, P. G. (1986). A model for the structure of lane-
changing decisions. Transportation Research Part B:
Methodological, 20(5):403–414.
Mirchevska, B., Pek, C., Werling, M., Althoff, M., and
Boedecker, J. (2018). High-level decision making for
safe and reasonable autonomous lane changing using
reinforcement learning.
Nilsson, J., Silvlin, J., Brannstrom, M., Coelingh, E., and
Fredriksson, J. (2016). If, When, and How to Perform
Lane Change Maneuvers on Highways. IEEE Intelli-
gent Transportation Systems Magazine, 8(4):68–78.
Shi, T., Wang, P., Cheng, X., Cha, C.-Y., and Huang, D.
(2019). Driving decision and control for autonomous
lane change based on deep reinforcement learning.
Toledo, T. (2003). Integrated Driving Behavior Modeling.
PhD thesis, MIT.
Ulbrich, S. and Maurer, M. (2013). Probabilistic Online
POMDP Decision Making for Lane Changes in Fully
Automated Driving. In 16th International IEEE Con-
ference on Intelligent Transportation Systems (ITSC
2013), pages 2063–2067.
Wang, P., Chan, C.-Y., and de La Fortelle, A. (2018). A
reinforcement learning based approach for automated
lane change maneuvers. In 2018 IEEE Intelligent Ve-
hicles Symposium (IV), pages 1379–1384.
Yavas, U., Kumbasar, T., and Ure, N. K. (2020). A new
approach for tactical decision making in lane chang-
ing: Sample efficient deep q learning with a safety
feedback reward. In 2020 IEEE Intelligent Vehicles
Symposium (IV), pages 1156–1161.
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