Chung, Hoam

Kalhara, Kasun

Kulíc, Dana

With the rapid development of the application of driverless vehicle technology, future roads will likely be dominated by commercial and personal Autonomous Vehicles (AVs). As the safety of Vulnerable Road Users (VRUs) such as pedestrians and bicycles is often at risk, precise behaviour prediction of VRUs in the local neighbourhood of AVs is essential for safetyaware decision making. This paper presents a Motion Prediction Module (MPM) that could be embedded into AVs and is capable of characterising dynamics, estimating intent and predicting future behaviour of the roadside agents in close proximity from a single-vehicle perspective. Motivated by the recent advances in Inverse Optimal Control (IOC) theory, a model-based prediction approach is proposed to estimate the agent's intentions from the past trajectory observations using Recovery Matrix IOC method. Subsequently, based on the recovered intentions, future trajectories are predicted by solving an optimal control problem. The MPM was deployed in both simulation and with experimental data and the intent estimation results show good accuracy (≈ 90%) while trajectory prediction results demonstrate high precision up to 3cm for both simulation and experimental test cases.</p

Motion prediction of roadside agents surrounding autonomous vehicles on the urban roads

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