On-Road Vehicle Trajectory Collection and Scene-Based Lane Change Analysis: Part II

Yao, Wen; Zeng, Qiqi; Lin, Yuping; Xu, Donghao; Zhao, Huijing; Guillemard, Franck; Géronimi, Stéphane; Aioun, François

doi:10.1109/tits.2016.2571724

Cited by 65 publications

(29 citation statements)

References 32 publications

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“…Based on [21], a fully probabilistic model is presented in [22]. The work in [23] presents experience-based data on the interaction between the ego and surrounding vehicles during lane changes. In order to consider interaction in situation assessment, one can compute an interaction-aware joint probability distribution [24] or detect conflicting intentions at intersections by comparing what vehicles intend to do with what they are expected to do [25].…”

Section: Introductionmentioning

confidence: 99%

Interaction-aware occupancy prediction of road vehicles

Koschi

Althoff

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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A crucial capability of autonomous road vehicles is the ability to cope with the unknown future behavior of surrounding traffic participants. This requires using nondeterministic models for prediction. While stochastic models are useful for long-term planning, we use set-valued nondeterminism capturing all possible behaviors in order to verify the safety of planned maneuvers. To reduce the set of solutions, our earlier work considers traffic rules; however, it neglects mutual influences between traffic participants. This work presents the first solution for establishing interaction within set-based prediction of traffic participants. Instead of explicitly modeling dependencies between vehicles, we trim reachable occupancy regions to consider interaction, which is computationally much more efficient. The usefulness of our approach is demonstrated by experiments from the CommonRoad benchmark repository.

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Section: Introductionmentioning

confidence: 99%

Interaction-aware occupancy prediction of road vehicles

Koschi

Althoff

2017

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC)

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“…Regarding the lane change maneuvers, the four factors are also important since situation awareness model enables the analysis of surrounding traffic flow and provide guidance to the DII system. In [117], driver lane change maneuver was classified into five categories based on the different interaction style with surrounding vehicles. With the analysis of 1000 naturalistic highway lane change data, it was found that 72% of the lane change was self-motivated and had no significant interaction with the surrounding vehicles.…”

Section: ) Situation Awareness Modelingmentioning

confidence: 99%

Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Xing

Wang

et al. 2019

IEEE Trans. Veh. Technol.

211

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Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

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“…Although the unsupervised methods have been successfully applied to segment the driving data based on their features, their focus is on operation behavior data, such as steering angle, brake pressure and accelerator pedal position, they do not concentrate on the particularity of trajectory segmentation. As for the vehicle trajectory analysis, Yao et al [22] established a lane change trajectory database to analysis the lane change behavior. The trajectories are extracted from the driving data by using the Support Vector Machines based classifier.…”

Section: Introductionmentioning

confidence: 99%

Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

Wang

Gong

Zhang

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously.

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On-Road Vehicle Trajectory Collection and Scene-Based Lane Change Analysis: Part II

Cited by 65 publications

References 32 publications

Interaction-aware occupancy prediction of road vehicles

Interaction-aware occupancy prediction of road vehicles

Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

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