Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving

Abstract

Accurately tracking and predicting behaviors of surrounding objects are key prerequisites for intelligent systems such as autonomous vehicles to achieve safe and high-quality decision making and motion planning. However, there still remain challenges for multi-target tracking due to object number fluctuation and occlusion. To overcome these challenges, we propose a constrained mixture sequential Monte Carlo (CMSMC) method in which a mixture representation is incorporated in the estimated posterior distribution to maintain multi-modality. Multiple targets can be tracked simultaneously within a unified framework without explicit data association between observations and tracking targets. The framework can incorporate an arbitrary prediction model as the implicit proposal distribution of the CMSMC method. An example in this paper is a learning-based model for hierarchical time-series prediction, which consists of a behavior recognition module and a state evolution module. Both modules in the proposed model are generic and flexible so as to be applied to a class of time-series prediction problems where behaviors can be separated into different levels. Finally, the proposed framework is applied to a numerical case study as well as a task of on-road vehicle tracking, behavior recognition, and prediction in highway scenarios. Instead of only focusing on forecasting trajectory of a single entity, we jointly predict continuous motions for interactive entities simultaneously. The proposed approaches are evaluated from multiple aspects, which demonstrate great potential for intelligent vehicular systems and traffic surveillance systems.Index Terms-Trajectory prediction, multi-target tracking, sequential Monte Carlo, probabilistic graphical models, deep learning, bahavior recognition.

Multiple Object Tracking in Deep Learning Approaches: A Survey

“…Li et al proposed a tracking model capable of hierarchical time series prediction and used constrained mix sequential Monte Carlo (CMSMC) [120] to solve the re-id problem. This paper consists of two modules: a behavior recognition module and a state evolution module.…”

Conditional Generative Neural System for Probabilistic Trajectory Prediction

“…However, such methods only suffice for short-term prediction in simple scenarios where interactions among entities can be ignored. More advanced learning-based models have been proposed to cope with more complicated scenarios, such as hidden Markov models [1], [2], Gaussian mixture regression [3], [4], Gaussian process, dynamic Bayesian networks, and rapidly-exploring random tree. However, these approaches are nontrivial to handle high-dimensional data and require hand-designed input features, which confines the flexibility of representation learning.…”

Review on Vehicle Detection Technology for Unmanned Ground Vehicles

“…Among various technologies, as vehicles are the most numerous and diverse targets in the driving environment, how to correctly identify vehicles has become a research hotspot for UGVs [ 8 ]. In the civil field, the correct detection of road vehicles can reduce traffic accidents, build a more complete ADAS [ 9 , 10 ] and achieve better integration with driver model [ 11 , 12 ], while in the field of military, the correct detection of military vehicle targets is of great significance to the battlefield reconnaissance, threat assessment and accurate attack in modern warfare [ 13 ].…”