Long short term memory for driver intent prediction

Zyner, Alex; Worrall, Stewart; Ward, James; Nebot, E.

doi:10.1109/ivs.2017.7995919

Cited by 132 publications

(68 citation statements)

References 15 publications

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“…They perform the same operations for every element of a sequence given the previous computation of the input sequence. Recently LSTMs have been used for predicting the driver intention and different LSTM-based architectures have been adopted; A simple LSTM with one or more layers is used in [5], [6], [7], [10]. Xin et al [8] deploy a dual LSTM.…”

Section: Deep-learning Based Methodsmentioning

confidence: 99%

“…Previous studies have tackled some aspects of the above challenges. In order to model the driver behavior, traditional data-driven techniques [1], [2], [3] as well as deep learning models based on Long Short Term Memories (LSTMs) [4], [5], [6], [7], [8], [9], [10] have been used. LSTM based encoder-decoder architectures have shown great success in modeling the non-linear temporal dependency between the input sequence elements.…”

Section: Introductionmentioning

confidence: 99%

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Non-local Social Pooling for Vehicle Trajectory Prediction

Messaoud

Yahiaoui

Verroust-Blondet

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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For an efficient integration of autonomous vehicles on roads, human-like reasoning and decision making in complex traffic situations are needed. One of the key factors to achieve this goal is the estimation of the future behavior of the vehicles present in the scene. In this work, we propose a new approach to predict the motion of vehicles surrounding a target vehicle in a highway environment. Our approach is based on an LSTM encoder-decoder that uses a social pooling mechanism to model the interactions between all the neighboring vehicles. The originality of our social pooling module is that it combines both local and non-local operations. The non-local multi-head attention mechanism captures the relative importance of each vehicle despite the inter-vehicle distances to the target vehicle, while the local blocks represent nearby interactions between vehicles. This paper compares the proposed approach with the state-of-the-art using two naturalistic driving datasets: Next Generation Simulation (NGSIM) and the new highD Dataset. The proposed method outperforms existing ones in terms of RMS values of prediction error, which shows the effectiveness of combining local and non-local operations in such a context.

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Section: Deep-learning Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Non-local Social Pooling for Vehicle Trajectory Prediction

Messaoud

Yahiaoui

Verroust-Blondet

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Dynamical motion modelling (DMM) is the general approach for the future location of pedestrians based on motion trajectory [202,215]. In [178], an Extended Kalman filter, a type of Bayesian filter, was used for short intent estimation (<2 s).…”

Section: Dmmmentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

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“…When we process the driving maneuvers prediction task by multi-source data fusion, LSTM [10] networks are widely used. In many applications about driver's behavior, LSTM performs better than traditional model and standard RNN [3,4,11,12,13,14,15]. Some previous works analyze the importance and superiority of LSTM for modeling of driver's behavior [11].…”

Section: Traditional Fusion Modelmentioning

confidence: 99%

Driving Maneuvers Prediction Based on Cognition-driven and Data-driven Method

Zhou

Dong

2018

2018 IEEE Visual Communications and Image Processing (VCIP)

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Advanced Driver Assistance Systems (ADAS) improve driving safety significantly. They alert drivers from unsafe traffic conditions when a dangerous maneuver appears. Traditional methods to predict driving maneuvers are mostly based on data-driven models alone. However, existing methods to understand the driver's intention remain an ongoing challenge due to a lack of intersection of human cognition and data analysis. To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model. We introduce a model named Cognitive Fusion-RNN (CF-RNN) which fuses the data inside the vehicle and the data outside the vehicle in a cognitive way. The CF-RNN model consists of two Long Short-Term Memory (LSTM) branches regulated by human reaction time. Experiments on the Brain4Cars benchmark dataset demonstrate that the proposed method outperforms previous methods and achieves state-of-the-art performance.

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Long short term memory for driver intent prediction

Cited by 132 publications

References 15 publications

Non-local Social Pooling for Vehicle Trajectory Prediction

Non-local Social Pooling for Vehicle Trajectory Prediction

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Driving Maneuvers Prediction Based on Cognition-driven and Data-driven Method

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