Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding

Park, Seong Hyeon; Lee, Gyubok; Seo, Ji-Min; Bhat, M. Seetharama; Kang, Minseok; Francis, Jonathan; Jadhav, Ashwin; Liang, Paul Pu; Morency, Louis–Philippe

doi:10.1007/978-3-030-58621-8_17

Cited by 73 publications

(36 citation statements)

References 25 publications

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“…There exist predefined test datasets only for Argoverse and nuScenes, which are thus the only datasets allowing a quantitative model performance comparison. An intra-dataset comparison shows that the Attention Network gives especially good performance [69,75,76].…”

Section: Discussionmentioning

confidence: 99%

“…Please note that at the time of submission, the Argoverse leaderboard [132] has a minimum average displacement error of 0.7897 m. Therefore, the result of minADE = 0.73 m in [76] is questionable.…”

Section: Discussionmentioning

confidence: 99%

“…A shift towards urban use cases was observable [76,78,82,123,124]: the difficulties are the variety of traffic participants and shorter time scales. A work that especially focuses on heterogeneous traffic is the 4D LSTM approach by Ma et al [125].…”

Section: Gnns Attention and New Use Casesmentioning

confidence: 99%

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A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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Towards the aim of mastering level 5, a fully automated vehicle needs to be equipped with sensors for a 360∘ surround perception of the environment. In addition to this, it is required to anticipate plausible evolutions of the traffic scene such that it is possible to act in time, not just to react in case of emergencies. This way, a safe and smooth driving experience can be guaranteed. The complex spatio-temporal dependencies and high dynamics are some of the biggest challenges for scene prediction. The subtile indications of other drivers’ intentions, which are often intuitively clear to the human driver, require data-driven models such as deep learning techniques. When dealing with uncertainties and making decisions based on noisy or sparse data, deep learning models also show a very robust performance. In this survey, a detailed overview of scene prediction models is presented with a historical approach. A quantitative comparison of the model results reveals the dominance of deep learning methods in current state-of-the-art research in this area, leading to a competition on the cm scale. Moreover, it also shows the problem of inter-model comparison, as many publications do not use standardized test sets. However, it is questionable if such improvements on the cm scale are actually necessary. More effort should be spent in trying to understand varying model performances, identifying if the difference is in the datasets (many simple situations versus many corner cases) or actually an issue of the model itself.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Review on Scene Prediction for Automated Driving

Stockem

Krüger

Stolpe

et al. 2022

Physics

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“…In addition, deep generative models are employed to learn the multimodalities of interactive behaviors, such as generative adversarial nets [47] and variational auto-encoder [35], [48]. Furthermore, attention mechanisms [49], [50] are incorporated into these models for modeling complex sequential patterns [38], [51], [52] and reinforcement learning are applied to teach agents to behave like human road users [35], [53]. However, most of the works simplify the interaction process of road users as road agents considering only their motion behavior, and the possibilities of communications and feedback among them are over simplified or neglected.…”

Section: B Effect Of Ehmi On the Autonomous Driving Systemsmentioning

confidence: 99%

Autonomous Vehicles Drive into Shared Spaces: eHMI Design Concept Focusing on Vulnerable Road Users

Li¹,

Cheng²,

Zeng³

et al. 2021

Preprint

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In comparison to conventional traffic designs, shared spaces promote a more pleasant urban environment with slower motorized movement, smoother traffic, and less congestion. In the foreseeable future, shared spaces will be populated with a mixture of autonomous vehicles (AVs) and vulnerable road users (VRUs) like pedestrians and cyclists. However, a driver-less AV lacks a way to communicate with the VRUs when they have to reach an agreement of a negotiation, which brings new challenges to the safety and smoothness of the traffic. To find a feasible solution to integrating AVs seamlessly into shared-space traffic, we first identified the possible issues that the shared-space designs have not considered for the role of AVs. Then an online questionnaire was used to ask participants about how they would like a driver of the manually driving vehicle to communicate with VRUs in a shared space. We found that when the driver wanted to give some suggestions to the VRUs in a negotiation, participants thought that the communications via the driver's body behaviors were necessary. Besides, when the driver conveyed information about her/his intentions and cautions to the VRUs, participants selected different communication methods with respect to their transport modes (as a driver, pedestrian, or cyclist). These results suggest that novel eHMIs might be useful for AV-VRU communication when the original drivers are not present. Hence, a potential eHMI design concept was proposed for different VRUs to meet their various expectations. In the end, we further discussed the effects of the eHMIs on improving the sociality in shared spaces and the autonomous driving systems.

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“…In [32], a new data set was proposed for Level 5 autonomous driving with 15,242 labelled data, the data set covers the task of trajectory prediction and planning in autonomous vehicles. In [33], a programmatic set of procedures was provided to convert the tracking data of nuScenes [34] to a new data set for trajectory forecasting while a new model was designed to address the lack of diversity and admissibility for trajectory forecasting through the understanding of the multimodal environmental context. In [35], a new data set covered 1150 scenes in Mountain View, Phoenix, and San Francisco with high‐quality LiDAR calibrator and camera device was proposed by the scientists and engineers from Google and Waymo.…”

Section: Related Workmentioning

confidence: 99%

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

Yan

Zifeng

Yin

et al. 2020

IET intell. transp. syst

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It is of great interest for autonomous vehicles to predict the trajectory of other vehicles when planning a safe trajectory. To accurately predict the trajectory of the target vehicle, the interaction between vehicles must be considered. Interaction aware prediction methods track the previous trajectories of both the target vehicle and its surrounding vehicles. In this study, the authors consider trajectory prediction as a sequence‐to‐sequence prediction problem. They tackle this problem with an LSTM encoder–decoder framework. Moreover, they propose two spatial‐attention mechanisms to account for the interaction between vehicles, i.e. context attention and lane attention. Spatial‐attention mechanisms adopt the selective‐attention mechanism of human drivers. They choose context vectors to help the model understand the surrounding environment better and thus improve its prediction accuracy. They evaluate the authors’ methods on the highD data set recorded in German highways with root mean squared error metric. Their experimental results show superior performance to other state‐of‐the‐art methods. Code is available at https://github.com/momo1986/Spatial-attention.

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Diverse and Admissible Trajectory Forecasting Through Multimodal Context Understanding

Cited by 73 publications

References 25 publications

A Review on Scene Prediction for Automated Driving

A Review on Scene Prediction for Automated Driving

Autonomous Vehicles Drive into Shared Spaces: eHMI Design Concept Focusing on Vulnerable Road Users

Trajectory prediction for intelligent vehicles using spatial‐attention mechanism

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