Predictive maneuver evaluation for enhancement of Car-to-X mobility data

Firl, Jonas; Stübing, Hagen; Huss, Sorin A.; Stiller, Christoph

doi:10.1109/ivs.2012.6232217

Cited by 48 publications

(19 citation statements)

References 13 publications

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“…Trajectory prediction has been researched extensively. Approaches include the Bayesian formulation [27], the Monte Carlo simulation [10], Hidden Markov Models (HMMs) [14], and Kalman Filters [23].…”

Section: Prediction Algorithms and Interactionsmentioning

confidence: 99%

TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

Chandra

Bhattacharya

Bera³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

255

188

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We present a new algorithm for predicting the near-term trajectories of road agents in dense traffic videos. Our approach is designed for heterogeneous traffic, where the road agents may correspond to buses, cars, scooters, bi-cycles, or pedestrians. We model the interactions between different road agents using a novel LSTM-CNN hybrid network for trajectory prediction. In particular, we take into account heterogeneous interactions that implicitly account for the varying shapes, dynamics, and behaviors of different road agents. In addition, we model horizon-based interactions which are used to implicitly model the driving behavior of each road agent. We evaluate the performance of our prediction algorithm, TraPHic, on the standard datasets and also introduce a new dense, heterogeneous traffic dataset corresponding to urban Asian videos and agent trajectories. We outperform state-of-the-art methods on dense traffic datasets by 30%. Code for our implementation can be found on our project webpage.

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Section: Prediction Algorithms and Interactionsmentioning

confidence: 99%

TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

Chandra

Bhattacharya

Bera³

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

255

188

View full text Add to dashboard Cite

show abstract

“…For example, the speed reported by the positioning sensors should be consistent with the vehicle velocity obtained from the speedometer. Plausibilityand consistency-based solutions [10,29,50,51,[55][56][57][58][59][60][61][62][63][64][65][66] can effectively detect many types of attacks under controlled conditions, in which accurate and reliable information can be collected. However, the vehicular environment is highly dynamic, and the collection of accurate and reliable information cannot be guaranteed.…”

Section: Related Workmentioning

confidence: 99%

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

et al. 2019

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Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles' scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications. IntroductionRoad collisions are increasing, and they are being expected to be the fifth leading cause of death by 2030 [1,2]. Annually, millions of people lose their lives on roads worldwide due to traffic accidents [1], with 40 times more suffering from injuries. These accidents are also the main cause of traffic congestion, which in turn has a great impact on the economy [3,4], and billions of dollars are lost due to the treatment of injuries, loss of property, lost working hours, and high fuel consumption [5]. Several studies reveal that more than 95%...

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“…Approaches reported in literature include Bayesian networks [8], Monte Carlo Simulation [9], Hidden Markov Models (HMM) [10], [11], [12], Support Vector Machines [13] and prototype based methods [14].…”

Section: A Related Workmentioning

confidence: 99%

“…The individual observations are not independent given H i , so a naive Bayesian approach would not model the underlying probability distribution correctly. Instead, we chose to average the individual scores from (12) to obtain…”

Section: B Comparison-based Approachmentioning

confidence: 99%

Velocity-Based Driver Intent Inference at Urban Intersections in the Presence of Preceding Vehicles

Liebner

Klanner

Baumann

et al. 2013

IEEE Intell. Transport. Syst. Mag.

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Abstract-Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver's intent can be inferred by evaluating the vehicle's speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behavior rather than by the need to slow down before doing a left or right turn. To infer the driver's intent under such circumstances, a simple, real-time capable approach using a parametric model to represent both car-following and turning behavior is proposed. The performance of two alternative parameterizations based on observations at an individual intersection and a generic curvature-based model is evaluated in combination with two different Bayes net classification algorithms. In addition, the driver model is shown to be capable of predicting the future trajectory of the vehicle.

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Predictive maneuver evaluation for enhancement of Car-to-X mobility data

Cited by 48 publications

References 13 publications

TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

Ensemble-Based Hybrid Context-Aware Misbehavior Detection Model for Vehicular Ad Hoc Network

Velocity-Based Driver Intent Inference at Urban Intersections in the Presence of Preceding Vehicles

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