Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Yi, Dewei; Su, Jinya; Liu, Cunjia

doi:10.1109/tii.2018.2890141

Cited by 48 publications

(31 citation statements)

References 31 publications

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“…According to [3,22,26,[30][31][32][33][34], driving assistance systems should be safe, effective, and comfortable. To meet these criteria, personalization is introduced to understand the status of a specific driver [35], and take individual driving styles [29], requirements, and preferences [36] into account.…”

Section: Personalization In Driving Assistancementioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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Section: Personalization In Driving Assistancementioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

Self Cite

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“…Unfortunately, we did not evaluate the performance nor have we considered spatial data. Decidedly, the literature includes several works related to trajectory clustering [21] and its applications on resolving real world problems [22]. Still, to the best of our knowledge, this is the first work discussing the exploit of the transitive closure on a fuzzy similarity relation to extract clusters of raw trajectories by using Spark.…”

Section: Literaturementioning

confidence: 99%

A Distributed Execution Pipeline for Clustering Trajectories Based on a Fuzzy Similarity Relation

Maguerra¹,

Boulmakoul²,

Karim

et al. 2019

Algorithms

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The proliferation of indoor and outdoor tracking devices has led to a vast amount of spatial data. Each object can be described by several trajectories that, once analysed, can yield to significant knowledge. In particular, pattern analysis by clustering generic trajectories can give insight into objects sharing the same patterns. Still, sequential clustering approaches fail to handle large volumes of data. Hence, the necessity of distributed systems to be able to infer knowledge in a trivial time interval. In this paper, we detail an efficient, scalable and distributed execution pipeline for clustering raw trajectories. The clustering is achieved via a fuzzy similarity relation obtained by the transitive closure of a proximity relation. Moreover, the pipeline is integrated in Spark, implemented in Scala and leverages the Core and Graphx libraries making use of Resilient Distributed Datasets (RDD) and graph processing. Furthermore, a new simple, but very efficient, partitioning logic has been deployed in Spark and integrated into the execution process. The objective behind this logic is to equally distribute the load among all executors by considering the complexity of the data. In particular, resolving the load balancing issue has reduced the conventional execution time in an important manner. Evaluation and performance of the whole distributed process has been analysed by handling the Geolife project’s GPS trajectory dataset.

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“…Numerous collisions and fatal accidents occurred at intersections in the United States, where an estimated 45% of injury crashes and 22% of roadway fatalities are intersection-related [ 14 ]. According to the EU community road accident database, in the past decade (2001–2010), intersection-related fatalities accounted for more than 20% in the EU [ 15 ]. The inability of drivers to assess correctly and/or observe dangerous situations is believed to be a major factor in these accidents [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

Zhang

2020

Sensors

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At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

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Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Cited by 48 publications

References 31 publications

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

A Distributed Execution Pipeline for Clustering Trajectories Based on a Fuzzy Similarity Relation

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

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