When will it change the lane? A probabilistic regression approach for rarely occurring events

Abstract

Understanding traffic situations in dynamic traffic environments is an essential requirement for autonomous driving. The prediction of the current traffic scene into the future is one of the main problems in this context. In this publication we focus on highway scenarios, where the maneuver space for traffic participants is limited to a small number of possible behavior classes. Even though there are many publications in the field of maneuver prediction, most of them set the focus on the classification problem, whether a certain maneuver is executed or not. We extend approaches which solve the classification problem of lane-change behavior by introducing the novel aspect of estimating a continuous distribution of possible trajectories.Our novel approach uses the probabilities which are assigned by a Random Decision Forest to each of the maneuvers lane following, lane change left and lane change right. Using measured data of a vehicle and the knowledge of the typical lateral movement of vehicles over time taken from realworlddata, we derive a Gaussian Mixture Regression method. For the final result we combine the predicted probability density functions of the regression method and the computed maneuver probabilities using a Mixture of Experts approach.In a large scale experiment on real world data collected on multiple test drives we trained and validated our prediction model and show the gained high prediction accuracy of the proposed method.

Convolutional Social Pooling for Vehicle Trajectory Prediction

“…However, due to the large variation in traffic configurations, this approach requires a large dataset for generalization. This approach has been used in prior works for the case of two vehicles approaching an intersection [9], and lateral motion prediction on highways [20]. We use the data-driven approach for intervehicle interaction in this paper, since it it not limited by the design of a hand-crafted cost function, and also due to the availability of large datasets of real freeway traffic [4,5].…”

Attention-based Lane Change Prediction

“…• Random Forest (RF) [3]: The concatenated features (G Z , G E , G M ) serve as input. • Naive Bayes (NB) [13]: The features m, v lat and relative velocity to preceding car are used.…”

“…They introduced two models, a Naive Bayesian approach, and a Hidden Markov Model on top of the Naive Bayesian model, with the vanilla Naive Bayesian approach performing better. In another work Schlechtriemen et al [3] tackled the problem of predicting trajectories, where they consider lane change prediction as a helping subtask. To achieve better generalization, they fed all the available features to a random forest.…”

“…For predicting lane changes, several "classical" models, like Support Vector Machines (SVMs) [2] or Random Forests [3], have been proposed, with only one recurrent neural net having been published recently [4]. These classical methods, though theoretically sound, see maneuver prediction as function estimation.…”

Sampling Based Motion Planning for Heavy Duty Autonomous Vehicles

“…The future trajectories of other vehicles are often predicted using the assumption of constant velocity (Ziegler et al, 2014b;Werling et al, 2008;Ferguson et al, 2008). This assumption is often valid in simpler situations but with increasing complexity of traffic situations, intentions such as lane changes, yielding at intersection or turning needs to be predicted (Liebner et al, 2013;Schlechtriemen et al, 2015;Ward and Folkesson, 2015). Further complicating the prediction is the dependency on our own actions.…”