Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network

Kim, ByeoungDo; Kang, Chang Mook; Kim, Jaekyum; Lee, Seung Hi; Chung, Chung Choo; Choi, Jun Won

doi:10.1109/itsc.2017.8317943

Cited by 345 publications

(190 citation statements)

References 12 publications

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“…LSTM can infer goals with a relatively high accuracy in a wide range of activities of daily living with similar situations. Further, a number of advantages of LSTM model for goal prediction, especially in dynamic environment like the smart home, were identified that are consistent with past research (Bai et al, 2018;Kim et al, 2017;Lipton et al, 2016;Peng & Lin, 2016).…”

Section: Discussionsupporting

confidence: 85%

“…It is important to note that the behavior of the gate control is data driven (i.e., learned from data). Also, an additional output network is added to the hidden state h t , this enables us to extract the information relevant to the given problem (Kim et al, ) . For example, since our case study is formulated as a multi‐class classification problem (see Figure ), to obtain the prediction class scores for a total of J classes at a time step t, a softmax layer comprising of the linear transformation of is added on top of the last LSTM layer L to estimate the posterior probability p j of the j ‐th class as follows:

p_{j} = italicsoftmax ((), h_{t}^{L}) = \frac{\exp ((), u_{j}^{T} h_{t}^{L} + b_{j})}{\sum_{j^{'} \in j} \exp ((), u_{j^{'}}^{T} h_{t}^{L} + b_{j^{'}})}

…”

Section: Situation‐centered Goal Reinforcement Frameworkmentioning

confidence: 99%

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Situation‐centered goal reinforcement of activities of daily living in smart home environments

2019

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Older adults with early‐stage dementia (ED) can experience confusion or lack clarity when performing routine activities of daily living (ADLs). These circumstances predispose the older adult to safety‐critical and often risky situations. A safety‐critical risky situation is one that constitutes a hazard. To support independent living, a sensor‐laden smart environment can be employed to mitigate such hazards. In this paper, we propose a situation‐centered goal reinforcement framework that supports older adults with ED in their decision making, and guides them through their ADL in order to fulfill their goal or intention and avoid hazards. First, we employ an LSTM (Long Short‐Term Memory) model to infer the current goal of the resident, using their previously observed normal ADL patterns. Secondly, we identify potentially risky situations in their currently observed goal path. We then incorporate a situ‐learning agent (SLA) that helps an inhabitant to make the right decision, thus preventing adverse events while guiding her through the task sequence that leads to her goal state. In addition, we use a naïve agent to simulate episodes of confusion similar to those that might be experienced by older adults with ED. We validated our method against an open‐source dementia dataset (Quesada et al., 2015) by considering four types of ADLs as case studies. We achieved an accuracy of 90.1% for our goal inference model, higher than the accuracies reported by related studies. We also reported other metrics including precision, recall and f1‐score for goal inference model. Finally, SLA's action recommendations relevance was evaluated accordingly.

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Section: Discussionsupporting

confidence: 85%

p_{j} = italicsoftmax ((), h_{t}^{L}) = \frac{\exp ((), u_{j}^{T} h_{t}^{L} + b_{j})}{\sum_{j^{'} \in j} \exp ((), u_{j^{'}}^{T} h_{t}^{L} + b_{j^{'}})}

…”

Section: Situation‐centered Goal Reinforcement Frameworkmentioning

confidence: 99%

Situation‐centered goal reinforcement of activities of daily living in smart home environments

2019

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“…Kim et al . [KKK*17] proposed an LSTM‐based probabilistic vehicle trajectory prediction approach which uses an occupancy grid map to characterize the driving environment. Deo and Trivedi [DT18] adopt a convolutional social pooling network to predict vehicle trajectories on highways.…”

Section: Applications In Autonomous Drivingmentioning

confidence: 99%

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Chao

et al. 2019

Computer Graphics Forum

105

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Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions.

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“…In [2], the authors modeled the driver behavior by hidden Markov models (HMM) and Gaussian Process (GP) to generate a group of future trajectories of the predicted vehicle. The long shortterm memory (LSTM) method is utilized in [1] and [3] to analyze past trajectory data and predict the future locations of the surrounding vehicles. [8] proposed to combine a modified mixture density network (MDN) [4] and a conditional variational autoencoder (CVAE) to predict both discrete intention and continuous motions for multiple interacting vehicles.…”

Section: A Motivationmentioning

confidence: 99%

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

Sun

Tomizuka

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Accurately predicting future behaviors of surrounding vehicles is an essential capability for autonomous vehicles in order to plan safe and feasible trajectories. The behaviors of others, however, are full of uncertainties. Both rational and irrational behaviors exist, and the autonomous vehicles need to be aware of this in their prediction module. The prediction module is also expected to generate reasonable results in the presence of unseen and corner scenarios. Two types of prediction models are typically used to solve the prediction problem: learning-based model and planning-based model. Learning-based model utilizes real driving data to model the human behaviors. Depending on the structure of the data, learning-based models can predict both rational and irrational behaviors. But the balance between them cannot be customized, which creates challenges in generalizing the prediction results. Planning-based model, on the other hand, usually assumes human as a rational agent, i.e., it anticipates only rational behavior of human drivers. In this paper, a generic prediction architecture is proposed to address various rationalities in human behavior. We leverage the advantages from both learningbased and planning-based prediction models. The proposed approach is able to predict continuous trajectories that wellreflect possible future situations of other drivers. Moreover, the prediction performance remains stable under various unseen driving scenarios. A case study under a real-world roundabout scenario is provided to demonstrate the performance and capability of the proposed prediction architecture.

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Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network

Cited by 345 publications

References 12 publications

Situation‐centered goal reinforcement of activities of daily living in smart home environments

Situation‐centered goal reinforcement of activities of daily living in smart home environments

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving

Generic Prediction Architecture Considering both Rational and Irrational Driving Behaviors

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