… inverse reinforcement learning with the proposed structural assumption to driving behavior
modeling from naturalistic highway driving data. … Since our model is a probabilistic model, we …

… We choose the top3 accuracy because our planning framework is probabilistic, which
can also address the stochasticity of human driving behaviors. In addition, we decrease the …

… is predicted with maximum entropy inverse reinforcement learning … driving behavior with
IRL and online updating of driver … , “Probabilistic prediction of interactive driving behavior via …

… Predicting driving behavior using inverse reinforcement learning with multiple reward …
Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement …

… In [22], a probabilistic prediction approach based on hierarchical inverse reinforcement learning
was … To intuitively reflect the randomness in human driving behavior, an expert trajectory …

… data with inverse reinforcement learning,” IEEE Transactions on Intelligent Transportation …
, “Probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement …

… and does not match human driving behavior in longer lanes. To … trajectory prediction models,
the ego vehicle’s behavior is … Since our model is a probabilistic model, we define the final …

… Tomizuka, “Probabilistic prediction of interactive driving behavior via hierarchical inverse
reinforcement learning,” in 2018 21st International Conference on Intelligent Transportation …

… prediction and human-like driving via inverse reinforcement learning from demonstrations,
and as those demonstrations are not on the margin of driving physics or collision with …

… DRIVING BEHAVIOR We apply the proposed SMIRL to learn the human driving behavior …
3) Probabilistic Metrics: We also evaluate the likelihood of the ground-truth trajectories given …