Behavioral Intention Prediction in Driving Scenes: A Survey

Fang, Jianwu; Wang, Fan; Xue, Jianru; Chua, Tat-Seng

doi:10.1109/tits.2024.3374342

Cited by 7 publications

(1 citation statement)

References 216 publications

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“…According to the road safety report published by the World Health Organization (WHO) in 2018, pedestrians account for 23% of the 1.35 million deaths globally caused by road traffic accidents [1]. Consequently, the prediction of pedestrian behavior remains an active area of research as evidenced by recent publications [2], [3], [4], [5], [6]. This line of research, known as pedestrian trajectory prediction, focuses on the modeling of their future trajectories and enables autonomous systems to consider their behavior [7].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Pedestrian Trajectory Prediction Methods With Respect to Autonomous Driving

Uhlemann,

Fent,

Lienkamp

2024

IEEE Trans. Intell. Transport. Syst.

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In this paper, we assess the state of the art in pedestrian trajectory prediction within the context of generating single trajectories, a critical aspect aligning with the requirements in autonomous systems. The evaluation is conducted on the widely-used ETH/UCY dataset where the Average Displacement Error (ADE) and the Final Displacement Error (FDE) are reported. Alongside this, we perform an ablation study to investigate the impact of the observed motion history on prediction performance. To evaluate the scalability of each approach when confronted with varying amounts of agents, the inference time of each model is measured. Following a quantitative analysis, the resulting predictions are compared in a qualitative manner, giving insight into the strengths and weaknesses of current approaches. The results demonstrate that although a constant velocity model (CVM) provides a good approximation of the overall dynamics in the majority of cases, additional features need to be incorporated to reflect common pedestrian behavior observed. Therefore, this study presents a data-driven analysis with the intent to guide the future development of pedestrian trajectory prediction algorithms.

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