Heterogeneity exists in car following behaviors due to the driver's habit, fatigue, distraction, or surrounding traffic. This research proposes a method of modeling and reasoning heterogeneous car-following behaviors based on a stochastic system, where scene vehicles are involved explicitly in addition to the traditional leader-follower pair in characterizing driving situations, a hidden variable (driver state) is introduced to conjugate driving situations to heterogeneous models in predicting a driver's acceleration control, and the dynamic procedure is described using a dynamic Bayesian network. Experiments are conducted using a large set of naturalistic driving data that were collected by driving an instrumented vehicle on the multi-lane motorways in Beijing, where four distinctive driver states are learnt from data, characterizing the car-following procedure with normal, slow responsive, strong and prompt responsive, and unresponsive behavioral styles. By using the proposed scene-aware multi-state model for acceleration prediction, the error is reduced to 0.19 m/s2in average compared with 0.29 m/s2of a single-state model. Influence of scene vehicles on a driver state and subsequently on velocity control is verified based on the data. To the best of our knowledge, this is the first work that explicitly incorporates scene vehicles as influential factors in a probabilistic approach for modeling and reasoning heterogeneous car-following behaviors, and the performance is demonstrated on a large set of naturalistic driving data.

This study proposes a multi-level trajectory analysis method for modeling traffic behavior from an ego-centric view, where on-road vehicle trajectories are collected based on the authors' previous studies of an on-board system consisting of multiple 2D lidar sensors. From an input set of trajectories, a set of hot regions (topics) that trajectory points most frequently present are first discovered using a sticky HDP-HMM; then, the major paths of the trajectories' transitions across different hot regions are extracted by recursively mining frequent subsequences of topics; and finally, paths are modeled using a hierarchical hidden Markov model (HHMM), where the intra-path dynamics is represented using an HMM, in which each state corresponds to a hot region, while the inter-path transition is assumed to be Markovian. The model could be used for behavior prediction, i.e. whenever a vehicle is detected in a scene, predicting which route it will probably follow and how its trajectory will probably develop over time, which is essential to interpreting the potential risks for longer time horizons. Experiments are conducted using a large set of vehicle trajectories collected from motorways in Beijing, and promising results are presented.

This research represents the heterogeneity in car following by a hidden variable driver state, which could change due to the driver's habit, fatigue, distraction, influence of surrounding traffic etc, resulting in the heterogeneous behaviors of such as fast or slow, strong or weak response to the same level of stimuli. A probabilistic method of driver state understanding is proposed by modeling and reasoning the heterogeneity in car-following behaviors, and the influence of surrounding traffic is addressed explicitly in addition to the leader-follower pair aiming at applications in crowded real-world traffic. Experiments are conducted by using the on-road trajectory data that were collected from motorways in Beijing, where four distinctive driver states and corresponding car-following models are learnt. With online understanding of driver state, the particular car-following model is used to predict the drivers velocity control, where results of improved accuracy are demonstrated.

This two-part paper aims to study lane change behaviors at the tactical level from an on-road perspective. Compared with longitudinal driving tasks, a lane change is more complicated because this task has more interactions with surrounding vehicles; thus, there are more potential risks during this procedure. Based on the results from Part I on an on-road vehicle trajectory collection, this part investigates lane change extraction and scene-based behavior analysis, and it has a particular focus on understanding the interactions between an ego and surrounding vehicles during the procedure. We claim that this paper provides the following novel contributions: 1) an automatic method is proposed for extracting lane change segments from a continuous driving sequence by modeling and recognizing patterns in a steering angle; 2) a lane change database at the trajectory level is generated, which reflects the interactions between an ego and the surrounding vehicles during the procedures; and 3) we present findings from analyzing lane change procedures using real-world data on the axes of both the ego's trajectory and interactions with the scene vehicles. To the authors' knowledge, this is the first lane change behavior study from an on-road perspective that addresses the vehicle interactions in real-world traffic at the trajectory level.

This two-part paper aims to study lane change behaviors at the tactical level from an on-road perspective, with a special focus on analyzing the interactions between an ego and surrounding vehicles during the procedure. Part I addresses vehicle trajectory collection, whereas Part II addresses lane change extraction and scene-based behavioral analysis. Different from the general technique of moving object detection and tracking, trajectory collection for tactical driving behavior study is required to have the properties of consistency, completeness, continuity, and accuracy. This paper proposes a system of on-road vehicle trajectory collection, where an instrumented vehicle is developed with multiple horizontal 2-D lidars that have 360° coverage. The software is developed by fitting the laser points of all lidars on a vehicle model using a coupled estimation of features and reliability along frames to achieve accurate state estimations of occluded data and robust data association in multiviewpoint sensing. The performance is investigated extensively, and a large trajectory set is developed through on-road driving at the Fourth Ring Road in Beijing for a total distance of 64 km, with more than 5700 environmental trajectories with a total length of over 19 h. The performance is demonstrated to be of high quality in terms of the required properties. To the authors' knowledge, this is the first system that is able to automatically collect all-around vehicle trajectories during on-road driving and to demonstrate good performance in providing a high-quality database for driving behavior studies from an on-road perspective that addresses vehicle interactions in real-world traffic at the trajectory level.