… an ego-vehicle speed prediction model using a long short-term memory (LSTM) based recurrent
neural network … accuracy: internal vehicle information, relative speed and distance to the …

… Furthermore, [12] proposes a recurrent neural network that predicts vehicle trajectories in
highway scenarios. Here, the prediction of the future motion is also represented in a grid, but it …

… analysis of vehicles surrounding the egovehicle is an … of observed on-road vehicles using
3D trajectory cues and a … on-road agents using a Recurrent Neural Network (RNN) and 3D …

… to use a recurrent neural network to predict a dynamic occupancy grid map, which divides
the vehicle … environment as DOGM in various scenarios with moving egovehicle. The used …

… However, the behavior of the traffic participants (eg the vehicles surrounding the egovehicle)
is often hard to predict since it is affected by various latent factors such as driver’s intention, …

… This paper studies the effectiveness of recurrent neural networks in predicting the
acceleration distributions for car … where y is the absolute longitudinal position of the egovehicle.The …

… 4, these models predict the speed of the forward path for the egovehicle to travel from the
current vehicle position to the prediction horizon. At the one prediction step, the sets of …

… A Recurrent Neural Network is then followed to examine the OF … of estimating the ego-motion
of an agent (eg, vehicle and robot) … -motion estimation by using recurrent neural networks …

… recurrent neural network (RNN) encoder-decoder model that separately captures both object
location and scale and pixellevel observations for future vehicle … ego vehicle, where vehicle …

… trajectories to induce the other vehicles to make space for the ego vehicle to cut in. Only
the ego vehicle uses the controller designed in Section II and the other vehicles (in blue) are …