Vehicle Trajectory Prediction Based on Motion Model and Maneuver Model Fusion with Interactive Multiple Models

Abstract

<div class="section abstract"><div class="htmlview paragraph">Safety is the cornerstone for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems (ADS). To assess the safety of a traffic situation, it is essential to predict motion states of traffic participants in the future with mathematic models. Accurate vehicle trajectory prediction is an important prerequisite for reasonable traffic situation risk assessment and appropriate decision making. Vehicle trajectory prediction methods can be generally divided into motion model based methods and maneuver model based methods. Vehicle trajectory prediction based on motion models can be accurate and reliable only in the short term. While vehicle trajectory prediction based on maneuver models present more satisfactory performance in the long term, these maneuver models rely on machine learning methods. Abundant data should be collected to train the maneuver recognition model, which increases complexity and lowers real-time performance. In this paper, a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) is proposed. Firstly, Constant Turn Rate and Acceleration (CTRA) motion model and Unscented Kalman Filter (UKF) are used to predict vehicle trajectory with uncertainty in the future. Then, vehicle trajectory prediction based on simplified maneuver recognition model is conducted, using temporal and spatial relationship between vehicle historical trajectory and lane lines. After that, vehicle trajectory prediction by integrating motion model and maneuver model with IMM is conducted. Finally, the proposed method is compared with CTRA motion model based vehicle trajectory prediction and lane keeping model (LKM) based vehicle trajectory prediction in two simulation test scenarios. The simulation results indicates that the IMM-based method achieves both excellent prediction accuracy and appropriate prediction uncertainty in the whole prediction horizon. This research can be used to support decision making for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving Systems and leads to improvement of traffic safety.</div></div>

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“…For example, Houenou et al (29) proposes to recognize the maneuver of the vehicle first, and then predict future trajectories using the Constant Yaw Rate and Acceleration motion model based on the recognized maneuver. Similar approaches can be found in many relevant studies (30)(31)(32)(33)(34). The maneuver that the model recognizes helps humans to understand how the model reaches its prediction.…”

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“…Te dynamic model is based on diferent forces acting on the vehicle during motion, such as longitudinal and lateral tire forces, to model the vehicle's motion [32]. Te kinematic model is based on the mathematical relationship between vehicle motion parameters, such as position, velocity, and acceleration, without considering the forces that afect motion [33]. In trajectory prediction research, because the internal parameters required by the dynamic model are difcult to observe with the vehicle's sensors, the use of the kinematic model is more common.…”

“…Study [44] proposed an LSTM network based on an encoder-decoder structure, which uses a convolutional network to extract vehicle spatial grid features and ultimately outputs a multimodal distribution of predicted trajectories. Study [33] introduced an encoder-decoder structure LSTM network based on spatiotemporal occupancy grid maps. Te maximum prediction duration can reach 2 seconds, but the training data for the above models need to be manually annotated, increasing the difculty of model training.…”

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“…In this algorithm, both methods of Kalman Filter and Recursive Least-Square work well to estimate the road slope and road friction coefficient. In [19], a vehicle trajectory prediction method based on motion model and maneuver model fusion with Interactive Multiple Model (IMM) was proposed. In the whole prediction range, this method not only has good prediction accuracy, but also has appropriate prediction uncertainty.…”