Using case-based reasoning for autonomous vehicle guidance

Vacek, Stefan; Gindele, Tobias; Zöllner, J. Marius; Dillmann, Rüdiger

doi:10.1109/iros.2007.4399298

Cited by 38 publications

(34 citation statements)

References 9 publications

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“…For different applications, ontology models should be defined based on corresponding domain knowledge. For autonomous driving, this technology has already been applied in some simple traffic scenarios [21,[24][25][26][27]. However, there is still no standard ontology for autonomous driving on urban roadways so far.…”

Section: A Novel Ontological Model For Scenario Descriptionmentioning

confidence: 99%

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Geng

Liang

et al. 2017

Applied Sciences

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Driving through dynamically changing traffic scenarios is a highly challenging task for autonomous vehicles, especially on urban roadways. Prediction of surrounding vehicles' driving behaviors plays a crucial role in autonomous vehicles. Most traditional driving behavior prediction models work only for a specific traffic scenario and cannot be adapted to different scenarios. In addition, priori driving knowledge was never considered sufficiently. This study proposes a novel scenario-adaptive approach to solve these problems. A novel ontology model was developed to model traffic scenarios. Continuous features of driving behavior were learned by Hidden Markov Models (HMMs). Then, a knowledge base was constructed to specify the model adaptation strategies and store priori probabilities based on the scenario's characteristics. Finally, the target vehicle's future behavior was predicted considering both a posteriori probabilities and a priori probabilities. The proposed approach was sufficiently evaluated with a real autonomous vehicle. The application scope of traditional models can be extended to a variety of scenarios, while the prediction performance can be improved by the consideration of priori knowledge. For lane-changing behaviors, the prediction time horizon can be extended by up to 56% (0.76 s) on average. Meanwhile, long-term prediction precision can be enhanced by over 26%.

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Section: A Novel Ontological Model For Scenario Descriptionmentioning

confidence: 99%

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Geng

Liang

et al. 2017

Applied Sciences

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“…Lee et al [12] proposed a method to detect lane color using support vector machine. However, the classifier based methods for lane type [5], [6], [7] are highly data dependent and computationally intensive and histogram methods [4] face issues in curvy road scenarios. Color segmentation methods for lane color [9], [10] suffer during illumination variations and image sensor settings which are very common in autonomous driving environment.…”

Section: A D V a N C E S I N I M A G E A N D V I D E O P R O C E S S mentioning

confidence: 99%

Lane Characterization Under Challenging Scenarios for Autonomous Driving System

Suddamalla

Singal

Mohan

2016

AIVP

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Advanced Driver Assistance systems (ADAS) have seen increasing popularity due to their importance in automotive driver safety and autonomous driving. These systems analyze data from several sensors mounted on the vehicle to detect lanes, obstacles and traffic conditions to ensure safe driving. Lane markings on road with different color and structure provide information on safe drive zone and other traffic restrictions on road. Typical ADAS solutions depend on vision based sensors for lane detection.Here we propose an efficient algorithm for detecting type and color of the lane marks as this information plays critical role in taking the decision for safety features such as lane change and lane keep assist. Our algorithm is pluggable to any state-of-art lane detection algorithm and provides lane type and color for straight, curvy roads. The proposed method is tested on various challenging scenarios and results are promising.

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“…최근 자동차에 비전기술을 적용하여 운전자의 안전운전지 원시스템에 대한 개발 및 연구가 활발히 진행되고 있다 [1][2][3][4][5][6][7][8]11]. 자동차 운전은 운전자의 시각정보에 의존하는 작업이 대략 90% 이상이기 때문에 운전자의 시각정보에 대한 처리 로노면 방향지시기호의 그래픽 모델 기반의 검출 [6], Hough 변환을 통한 검출 연구 [7], 역원근변환을 통한 연구 [8], 에지 탬플릿 매칭 [15] …”

Section: 서 론1 )unclassified

Detection of Direction Indicators on Road Surfaces Using Inverse Perspective Mapping and NN

Kim¹

2015

KIPS Transactions on Software and Data Engineering

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This paper proposes a method for detecting the direction indicator shown in the road surface efficiently from the black box system installed on the vehicle. In the proposed method, the direction indicators are detected by inverse perspective mapping(IPM) and bag of visual features(BOF)-based NN classifier. In order to apply the proposed method to real-time environments, the candidated regions of direction indicator in an image only performs IPM, and BOF-based NN is used for the classification of feature information from direction indicators. The results of applying the proposed method to the road surface direction indicators detection and recognition, the detection accuracy was presented at least about 89%, and the method presents a relatively high detection rate in the various road conditions. Thus it can be seen that the proposed method is applied to safe driving support systems available.

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Using case-based reasoning for autonomous vehicle guidance

Cited by 38 publications

References 9 publications

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

A Scenario-Adaptive Driving Behavior Prediction Approach to Urban Autonomous Driving

Lane Characterization Under Challenging Scenarios for Autonomous Driving System

Detection of Direction Indicators on Road Surfaces Using Inverse Perspective Mapping and NN

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