Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

HammerDrive: A task-aware driving visual attention model

We introduce HammerDrive, a novel architecture for task-aware visual attention prediction in driving. The proposed architecture is learnable from data and can reliably infer the current focus of attention of the driver in real-time, while only requiring limited and easy-to-access telemetry data from the vehicle. We build the proposed architecture on two core concepts: 1) driving can be modeled as a collection of sub-tasks (maneuvers), and 2) each sub-task affects the way a driver allocates visual attention resources, i.e., their eye gaze fixation. HammerDrive comprises two networks: a hierarchical monitoring network of forward-inverse model pairs for sub-task recognition and an ensemble network of task-dependent convolutional neural network modules for visual attention modeling. We assess the ability of HammerDrive to infer driver visual attention on data we collected from 20 experienced drivers in a virtual reality-based driving simulator experiment. We evaluate the accuracy of our monitoring network for sub-task recognition and show that it is an effective and light-weight network for reliable real-time tracking of driving maneuvers with above 90% accuracy. Our results show that HammerDrive outperforms a comparable state-of-the-art deep learning model for visual attention prediction on numerous metrics with ~13% improvement for both Kullback-Leibler divergence and similarity, and demonstrate that task-awareness is beneficial for driver visual attention prediction

Probabilistic Framework for Behavior Characterization of Traffic Participants Enabling Long Term Prediction

This research aims at developing new methods that predict the behaviors of the human driven traffic participants to enable safe operation of autonomous vehicles in complex traffic environments. Autonomous vehicles are expected to operate amongst human driven conventional vehicles in the traffic at least for the next few decades. For safe navigation they will need to infer the intents as well as the behaviors of the human traffic participants using extrinsically observable information, so that their trajectories can be predicted for a time horizon long enough to do a predictive risk analysis and gracefully avert any risky situation. This research approaches this challenge by recognizing that any maneuver performed by a human driver can be divided into four stages that depend on the surrounding context: intent determination, maneuver preparation, gap acceptance and maneuver execution. It builds on the hypothesis that for a given driver, the behavior not only spans across these four maneuver stages, but across multiple maneuvers. As a result, identifying the driver behavior in any of these stages can help characterize the nature of all the subsequent maneuvers that the driver is likely to perform, thus resulting in a more accurate prediction for a longer time horizon. To enable this, a novel probabilistic framework is proposed that couples the different maneuver stages of the observed traffic participant together and associates them to a driving style. To realize this framework two candidate Multiple Model Adaptive Estimation approaches were compared: Autonomous Multiple Model (AMM) and Interacting Multiple Model(IMM) filtering approach. The IMM approach proved superior to the AMM approach and was eventually validated using a trajectory extracted from a real world dataset for efficacy. The proposed framework was then implemented by extending the validated IMM approach with contextual information of the observed traffic participant. The classification of the driving style of the traffic participant (behavior characterization) was then demonstrated for two use case scenarios. The proposed contextual IMM (CIMM) framework also showed improvements in the performance of the behavior classification of the traffic participants compared to the IMM for the identified use case scenarios. This outcome warrants further exploration of this framework for different traffic scenarios. Further, it contributes towards the ongoing endeavors for safe deployment of autonomous vehicles on public roads

Drivers overtaking cyclists and pedestrians: Modeling road-user behavior for traffic safety

In a world aiming to shift to more sustainable modes of transportation, vulnerable road users (VRUs) like cyclists and pedestrians are still confronted with significant barriers to safety, particularly on rural roads where overtaking maneuvers represent a frequent and dangerous interaction with motorized traffic. If drivers misjudge their kinematics, even near-crashes without physical contact can harm the perceived safety of the VRU, which may decrease the willingness to continue cycling or walking on these roads. Crash risks when overtaking VRUs exist in different overtaking phases: when approaching the VRU, steering out, passing, and eventually returning. To make overtaking VRUs safer, improvements to policymaking, infrastructure, and vehicles are needed. However, these improvements need models that can describe or predict road-user behavior in overtaking, which was the objective of this thesis. Based on data sets obtained from a test-track experiment, field-test studies, and naturalistic studies, this thesis developed behavioral models for both objective and perceived safety of drivers and VRUs in different overtaking phases. The results indicate that drivers’ and VRUs’ behavior is mainly influenced by their highest crash or injury risk. The descriptive models showed that a close oncoming vehicle could reduce a driver’s safety margins to the VRU in all phases. Furthermore, the VRU behavior may affect the driver’s behavior; for instance, through lane positioning and, for pedestrians, walking direction. Infrastructure design and policymaking should focus on preventing overtaking in areas where oncoming vehicles are hard to estimate and enforcing sufficient clearances to the cyclist, stratified by speed. The predictive models can help vehicle safety systems adapt to drivers to become more acceptable, for instance, when assisting drivers in the decision to overtake or not. They may further help optimize road networks’ objective and perceived safety

Human-Like Decision-Making for Automated Driving in Highways

International audienc