Curvilinear-Coordinate-Based Object and Situation Assessment for Highly Automated Vehicles

Kim, Junsoo; Jo, Kichun; Lim, Wontaek; Lee, Min Chul; Sunwoo, Myoungho

doi:10.1109/tits.2014.2369737

Cited by 45 publications

(17 citation statements)

References 34 publications

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“…However, readers can refer to Ref. [ 25 ] for further details. After obtaining the curvilinear coordinates, we can assign the lane number to which the surrounding vehicle belongs to.…”

Section: Problem Statement and Methodologymentioning

confidence: 99%

Road-Aware Trajectory Prediction for Autonomous Driving on Highways

Yoon

Kim

Lee

et al. 2020

Sensors

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For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

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“…However, readers can refer to Ref. [ 25 ] for further details. After obtaining the curvilinear coordinates, we can assign the lane number to which the surrounding vehicle belongs to.…”

Section: Problem Statement and Methodologymentioning

confidence: 99%

Road-Aware Trajectory Prediction for Autonomous Driving on Highways

Yoon

Kim

Lee

et al. 2020

Sensors

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“…The global Cartesian coordinates (X, Y) and the road-aligned coordinates (s, n) are used concurrently. The relationship between two different coordinates can be easily interpreted through a curvilinear coordinate conversion as [34][35][36] f :…”

Section: Initializationmentioning

confidence: 99%

A probabilistic optimization approach for motion planning of autonomous vehicles

Kim

Lim

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part D:

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This paper presents a novel probabilistic approach for improving the motion planning performance of autonomous driving. The proposed approach is based on the sampling-based planning algorithm, which generates an optimal trajectory from a set of trajectory candidates. In order to treat the uncertainty in the perception data and the vehicle system, the particle filter framework is applied to the motion planning algorithm with four main steps: the time update of the trajectory candidates, the perception measurement update, the trajectory selection and the motion goal resampling. Since the proposed planning algorithm recursively generates an optimal trajectory, the time update of the trajectory candidate updates the motion goals of the trajectory candidates in the previous step using the vehicle model, and it also generates a new set of candidates. In order to evaluate the optimality of each candidate with regard to the safety and the reliability, a perception measurement update is performed. In this step, the importance weight of each candidate is computed using perception data and its adaptive likelihood function. Based on the candidates with updated importance weights, an optimal trajectory is determined in the trajectory selection. Then, the motion goal resampling modifies the set of motion goals based on the importance weights for efficient management of the motion goals in the iterative planning algorithm. The developed algorithm is validated using various types of test. The results show that the proposed method not only provides an integrated probabilistic interface between the perception and the planning but also results in an excellent performance in terms of the computation efficiency.

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“…Situation assessment evaluates some factors in the current scene to predict the changes in the next stage [9]. It provides a systematic way for us to understand the situation between our side and the enemy.…”

Section: Introductionmentioning

confidence: 99%