Self-aware Pedestrians Modeling for Testing Autonomous Vehicles in Simulation

Jan, Qazi Hamza; Kleen, Jan; Berns, Karsten

doi:10.5220/0009377505770584

Cited by 5 publications

(1 citation statement)

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“…Additionally, alternative approaches exist that explicitly consider the vehicle's influence on pedestrians' future states. In [78], the pedestrian's speed and direction are selected at each time step to ensure a collision-free trajectory when their paths intersect with the vehicle. In [79], the impact of the vehicle on the pedestrian's velocity is considered by incorporating an assessment of the collision risk.…”

Section: Explicit Interaction Modelingmentioning

confidence: 99%

Holistic Spatio-Temporal Graph Attention for Trajectory Prediction in Vehicle–Pedestrian Interactions

Alghodhaifi,

Lakshmanan

2023

Sensors

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Ensuring that intelligent vehicles do not cause fatal collisions remains a persistent challenge due to pedestrians’ unpredictable movements and behavior. The potential for risky situations or collisions arising from even minor misunderstandings in vehicle–pedestrian interactions is a cause for great concern. Considerable research has been dedicated to the advancement of predictive models for pedestrian behavior through trajectory prediction, as well as the exploration of the intricate dynamics of vehicle–pedestrian interactions. However, it is important to note that these studies have certain limitations. In this paper, we propose a novel graph-based trajectory prediction model for vehicle–pedestrian interactions called Holistic Spatio-Temporal Graph Attention (HSTGA) to address these limitations. HSTGA first extracts vehicle–pedestrian interaction spatial features using a multi-layer perceptron (MLP) sub-network and max pooling. Then, the vehicle–pedestrian interaction features are aggregated with the spatial features of pedestrians and vehicles to be fed into the LSTM. The LSTM is modified to learn the vehicle–pedestrian interactions adaptively. Moreover, HSTGA models temporal interactions using an additional LSTM. Then, it models the spatial interactions among pedestrians and between pedestrians and vehicles using graph attention networks (GATs) to combine the hidden states of the LSTMs. We evaluate the performance of HSTGA on three different scenario datasets, including complex unsignalized roundabouts with no crosswalks and unsignalized intersections. The results show that HSTGA outperforms several state-of-the-art methods in predicting linear, curvilinear, and piece-wise linear trajectories of vehicles and pedestrians. Our approach provides a more comprehensive understanding of social interactions, enabling more accurate trajectory prediction for safe vehicle navigation.

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Section: Explicit Interaction Modelingmentioning

confidence: 99%