… planning problem of the autonomous vehicle, and that it avoids collisions by considering the
future state of the other cars… a probabilistic modeling to predict future trajectories of vehicles. …

… model of cars in a merging-giveaway scenario. A pair of merging and through cars interaction
is … Dillmann, “Probabilistic decisionmaking under uncertainty for autonomous driving using …

… The goal of the ego vehicle is to plan its reference speed while obeying the traffic … the
probability of reaching s after taking action a from s. Similarly, Z(o,s ,a) = Pr(o|s ,a) is the probability …

We address ethical dilemma situations that may arise during autonomous driving. To evaluate
how this deliberation could work, we propose a decision-making algorithm based on a …

… an action, and p is a probability. The logic specifies that when … at a probability of p (in the
following, we will omit the probabilities … Probabilistic MDPbehavior planning for cars. In Proc. the …

… of the vehicle. We propose efficient decision-making and motion planning based on
probabilistic … To improve safety within the near future, probabilistic predictions are employed that …

… between vehicles, which in turn decreased the probability of … in approaching vehicles’
speed, the probability of pedestrians … besides vehicle speed, pedestrian distance to the vehicle …

… modeling of pedestrian and vehicle maneuvers in near misses … the probability of a transition
from state s to state s ′ taking actions ( a 1 , … , a N ). η is a subset of transition probabilities …

… Moreover, these methods suffer from a non-linearity issue of vehicle dynamics, that causes
… to explore an empty parking spot, plan then park a car in a random parking spot by avoiding …

… is a map from a given state to a probability distribution for each possible action. The agent
… One stopped vehicle is placed in front of the ego-vehicle’s initial position. The other vehicles …