… ' actions to master conflicts occurring in everyday driving situations. Without a … Monte Carlo
Tree Search (MCTS). We motivate why MCTS is particularly suited for the autonomous driving …

… A sensitivity analysis used calibration sets of various sizes in a Monte Carlo simulation to
resample sites. Results indicated that the current HSM criterion of 30 to 50 sites was insufficient …

… fact that drivers have most of their attention directed forward and are more likely to detect
other objects in this region. Furthermore, drivers are much more inclined to adapt their driving …

… models for predicting counts for each of the above crash types as a function of the daily
volume, segment length, speed limit and lane/shoulder width using Markov Chain Monte Carlo …

… driving cycle is constructed based on TPM and Monte Carlo method. Through the comparison
of driving … and NEDC with real-world driving data, the constructed driving cycle is proved to …

… Monte Carlo simulation. The proposed approach enables stochastic forecasts incorporating
uncertain driver … The test route consists of urban, rural and highway roads. Initial values for …

… In [34], the inverse reinforcement learning is implemented in a highway driving simulator
to avoid collisions. Paper [35] employs a deep Q-network to derive the steering and throttle …

… Driver Model (EDM) was recently developed to predict the vehicle speed for freeway driving,
car … model was used to analyze the effects of driving styles on fuel economy via Monte Carlo …

… of vehicle states using information about driving dynamics represented by multifractal …
Monte Carlo simulation was performed for a specific time, with a requirement concerning driving …

… to derive a driving policy for self-driving cars without relying on any human driving data or
rules … However, it is feasible to approximate it with the Monte Carlo method. We first sample Q …