… This thesis investigates pedestrianvehicle interactions at two … Additionally, vehicles have to
slow down more frequently for … distracted pedestrians and the vehicles interacting with them. It …

… as a stationary battery, electrical vehicle (EV) charging, and … 1 are based on MDP behavior.
For each decision stage, we … We use the transition probabilities ρ ( g , s g s | s g − 1 s ) to find …

… by the trajectory planner module, which plans the vehicle’s transition … planning is to sample
the state space using rapidly exploring trees(RRTs) or probabilistic sampling (eg Probabilistic …

… probabilities among an MDP’s states. In each state, transitions to other states are given by a
probability … Finite numbers of states again give rise to discrete, finitely supported probability …

… Each grid cell occupies with a probability for the existence of an obstacle at the particular
location. The size of such maps is limited to a local area around the vehicle, and the size …

… path planning algorithms implemented in motion planning for … making, motion planning, and
control for self-driving cars, in … decision making using probabilistic planning formalisms, such …

… of motion planning in reducing motion sickness in automated road vehicles. However, these
work … In this work, the attention will be turned on motion planning of autonomous vehicles, in …

… Domain experts may have mental models of MDP behavior … in Mountain Car store energy
by rocking the car back and … for Mountain Car with a parameter controlling the probability of …

… By keeping a constant number of vehicle described in each … of vehicles, at the expense of
removing some of the vehicles from the … Probabilistic online POMDP decision making for lane …

… Campbell, “Discrete and continuous, probabilistic anticipation … Dillmann, “Probabilistic
mdp-behavior planning for cars,” in … reliable path planning for the autonomous vehicle verdino,” …