Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Abstract

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97 % and a median lateral prediction error of only 0.18 m on a prediction horizon of 5 s.

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks

“…An overview of motion prediction approaches is presented in [1], which distinguishes three categories: physicsbased, maneuver-based, and interaction-aware approaches. Maneuver-based approaches, which are most relevant in the context of our work, typically define three fundamental maneuver classes: lane change to the left LCL, lane change to the right LCR, and lane following F LW [2]- [4]. These maneuver classes are used to simplify modeling the entirety of highway driving and its multimodality.…”

A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions

“…A good overview on corresponding approaches is provided in [16]. By contrast, maneuver-based approaches (e. g. [1], [17]- [22]) try to infer the maneuver a driver intends to perform. Finally, interaction-aware approaches [23]- [26] provide the most advanced motion models by predicting the motions of all vehicles in a given situation simultaneously.…”

“…While maneuver prediction approaches (e. g. [20], [24]) try to infer which one of a fixed set of maneuvers a vehicle will perform, position prediction approaches (e. g. [21], [23], [25]- [28]) try to infer at which exact position a vehicle will be at a certain future point in time, i. e. the latter approaches operate in a continuous space. Finally, hybrid approaches (e. g. [1], [17]- [19], [22]) integrate the outputs of maneuver and position prediction approaches into a single or combined model.…”

“…Although this example does not refer to external conditions, the same effects could be observed for such, when collecting more data e. g. through a fleet of several vehicles. 1 https://www.researchgate.net/publication/343392786 Prediction and Memory Module within AR display 2 For the sake of simplicity we restricted ourselves to solely investigate the lateral direction here.…”

“…Thereby, modules predicting future motions of surrounding traffic participants constitute a central piece of such system's intelligence. As shown in [1], it is beneficial to integrate external information such as knowledge about weather or traffic conditions into these prediction modules. Therefore, the systems are enabled to deal with rarely occuring and nevertheless challenging conditions, resulting in increased system performances as well as benefits for the drivers in general and especially during challenging conditions.…”