Decision Making under Uncertainty for Automated Vehicles in Urban Situations

Decision Making under Uncertainty for Automated Vehicles in Urban Situations

Chandiramani, Jayesh (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Delft Center for Systems and Control)

Baldi, S. (mentor)
Sefati, Mohsen (mentor)
Keviczky, T. (graduation committee)
Ferranti, L. (graduation committee)
Happee, R. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2017-11-07

One of the most crucial links to building an autonomous system is the task of decision making. The ability of a vehicle to make robust decisions on its own by predicting and assessing future consequences is what makes it intelligent. This task of decision making becomes even more complex due to the fact that the real world is uncertain, continuous and vehicles interact with each other. Sensors that perceive the real world and measure quantities such as position and speed of other traffic objects are inherently noisy and are further susceptible to external conditions. On the other hand, the road users’ intentions are stochastic and not measurable, and the presence of partial or complete vision based occlusions can make any measurements obtained useless. The decision making unit thus has to be aware of these issues and use the limited knowledge available to anticipate future situations that could unfold in an infinite number of ways to then maximize a reward (or minimize a cost).

In this thesis, a method to make automated longitudinal decisions along a predetermined path for autonomous vehicles in unsignalized urban scenarios is proposed. The decision mak- ing problem is formulated as a continuous Partially Observable Markov Decision Process (POMDP) with a discrete Bayesian Network estimating the behavior of other traffic objects. The future evolution of states are predicted using a multi-model Trajectory Estimator along with the digital map of the current driving area. Since continuous spaces make the belief space infinitely large, calculation of the value function for this problem becomes computa- tionally intractable. The presented solver algorithm approximates the value function instead of computing it directly, and additional optimizations reduce the belief space exploration area in order to improve performance. The results show that this single generalized framework is able to handle all the tested urban scenarios with a good safety margin in scenes with multiple traffic objects, even under zero visibility of the other traffic objects due to the presence of occlusions.

Autonomous driving
Decision making
Uncertainty
POMDP
Bayesian networks
Occlusion

http://resolver.tudelft.nl/uuid:b8339e46-0fde-48bc-a050-ec204c208e20

Student theses

master thesis

© 2017 Jayesh Chandiramani