ICPR 2024 Competition on Rider Intention Prediction

• URL: http://arxiv.org/abs/2503.08437v1
• Date: Tue, 11 Mar 2025 13:50:37 GMT
• Title: ICPR 2024 Competition on Rider Intention Prediction
• Authors: Shankar Gangisetty, Abdul Wasi, Shyam Nandan Rai, C. V. Jawahar, Sajay Raj, Manish Prajapati, Ayesha Choudhary, Aaryadev Chandra, Dev Chandan, Shireen Chand, Suvaditya Mukherjee,
• Abstract summary: Rider intention prediction (RIP) competition aims to address challenges in rider safety by proactively predicting maneuvers before they occur.<n>We collect a new dataset, namely, rider action anticipation dataset (RAAD) for the competition consisting of two tasks: single-view RIP and multi-view RIP.<n>For the competition, we received seventy-five registrations and five team submissions for inference of which we compared the methods of the top three performing teams on both the RIP tasks.
• Score: 19.858523089237675
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent surge in the vehicle market has led to an alarming increase in road accidents. This underscores the critical importance of enhancing road safety measures, particularly for vulnerable road users like motorcyclists. Hence, we introduce the rider intention prediction (RIP) competition that aims to address challenges in rider safety by proactively predicting maneuvers before they occur, thereby strengthening rider safety. This capability enables the riders to react to the potential incorrect maneuvers flagged by advanced driver assistance systems (ADAS). We collect a new dataset, namely, rider action anticipation dataset (RAAD) for the competition consisting of two tasks: single-view RIP and multi-view RIP. The dataset incorporates a spectrum of traffic conditions and challenging navigational maneuvers on roads with varying lighting conditions. For the competition, we received seventy-five registrations and five team submissions for inference of which we compared the methods of the top three performing teams on both the RIP tasks: one state-space model (Mamba2) and two learning-based approaches (SVM and CNN-LSTM). The results indicate that the state-space model outperformed the other methods across the entire dataset, providing a balanced performance across maneuver classes. The SVM-based RIP method showed the second-best performance when using random sampling and SMOTE. However, the CNN-LSTM method underperformed, primarily due to class imbalance issues, particularly struggling with minority classes. This paper details the proposed RAAD dataset and provides a summary of the submissions for the RIP 2024 competition.

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