Pedestrian Prediction by Planning Using Deep Neural Networks

Rehder, Eike; Wirth, Florian; Lauer, Martin; Stiller, Christoph

doi:10.1109/icra.2018.8460203

Cited by 110 publications

(73 citation statements)

References 19 publications

(31 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We subdivided the sequences using a fixed length and a frame rate of 5Hz, i .e. 0.2 second interval between each input, (the original frame rate of the dataset in [38] was 10Hz). In order to augment the data we used a sliding window of 1 time step (varying 0.1 seconds).…”

Section: Methodsmentioning

confidence: 99%

Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis

Ridel

Deo

Wolf

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Pedestrians and vehicles often share the road in complex inner city traffic. This leads to interactions between the vehicle and pedestrians, with each affecting the other's motion. In order to create robust methods to reason about pedestrian behavior and to design interfaces of communication between self-driving cars and pedestrians we need to better understand such interactions. In this paper, we present a datadriven approach to implicitly model pedestrians' interactions with vehicles, to better predict pedestrian behavior. We propose a Long Short-Term Memory (LSTM) model that takes as input the past trajectories of the pedestrian and ego-vehicle, and pedestrian head orientation, and predicts the future positions of the pedestrian. Our experiments based on a real-world, inner city dataset captured with vehicle mounted cameras, show that the usage of such cues improve pedestrian prediction when compared to a baseline that purely uses the past trajectory of the pedestrian. * This work was done when Daniela A. Ridel was a visiting scholar at the Laboratory for Intelligent and Safe Automobiles

show abstract

Section: Methodsmentioning

confidence: 99%

Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis

Ridel

Deo

Wolf

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…However, Pendleton et al () do not include a review on deep learning technologies, although the state‐of‐the‐art literature has revealed an increased interest in using deep learning technologies for path planning and behavior arbitration. Following, we discuss two of the most representative deep learning paradigms for path planning, namely IL (Grigorescu, Trasnea, Marina, Vasilcoi, & Cocias, ; Rehder, Quehl, & Stiller, ; Sun, Peng, Zhan, & Tomizuka, ) and DRL‐based planning (Paxton, Raman, Hager, & Kobilarov, ; L. Yu, Shao, Wei, & Zhou, ).…”

Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

“…The goal in IL (Grigorescu et al, ; Rehder et al, ; Sun et al, ) is to learn the behavior of a human driver from recorded driving experiences (Schwarting, Alonso‐Mora, & Rus, ). The strategy implies a vehicle teaching process from human demonstration.…”

Section: Deep Learning For Path Planning and Behavior Arbitrationmentioning

confidence: 99%

A survey of deep learning techniques for autonomous driving

Grigorescu

Trasnea

Cocias

et al. 2019

Journal of Field Robotics

1,214

495

View full text Add to dashboard Cite

The last decade witnessed increasingly rapid progress in self‐driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence (AI). The objective of this paper is to survey the current state‐of‐the‐art on deep learning technologies used in autonomous driving. We start by presenting AI‐based self‐driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration, and motion control algorithms. We investigate both the modular perception‐planning‐action pipeline, where each module is built using deep learning methods, as well as End2End systems, which directly map sensory information to steering commands. Additionally, we tackle current challenges encountered in designing AI architectures for autonomous driving, such as their safety, training data sources, and computational hardware. The comparison presented in this survey helps gain insight into the strengths and limitations of deep learning and AI approaches for autonomous driving and assist with design choices.

show abstract

“…IRL is conditioned on the goal destination of the VRU, but it is possible to estimate the goal online jointly with the future path. For instance, Rehder et al [8] estimate the goal using a neural net that combines image data with the previous path of the VRU. Ballan et al [9] learn preferred routes directly on image data rather than the semantic information, and show that the learned knowledge is transferable to new locations.…”

Section: Previous Workmentioning

confidence: 99%

Context-based cyclist path prediction using Recurrent Neural Networks

Pool

Kooij

Gavrila

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

This paper proposes a Recurrent Neural Network (RNN) for cyclist path prediction to learn the effect of contextual cues on the behavior directly in an end-to-end approach, removing the need for any annotations. The proposed RNN incorporates three distinct contextual cues: one related to actions of the cyclist, one related to the location of the cyclist on the road, and one related to the interaction between the cyclist and the egovehicle. The RNN predicts a Gaussian distribution over the future position of the cyclist one second into the future with a higher accuracy, compared to a current state-of-the-art model that is based on dynamic mode annotations, where our model attains an average prediction error of 33 cm one second into the future.

show abstract

Pedestrian Prediction by Planning Using Deep Neural Networks

Cited by 110 publications

References 19 publications

Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis

Understanding Pedestrian-Vehicle Interactions with Vehicle Mounted Vision: An LSTM Model and Empirical Analysis

A survey of deep learning techniques for autonomous driving

Context-based cyclist path prediction using Recurrent Neural Networks

Contact Info

Product

Resources

About