Survey of the Application Fields and Modeling Methods of Automotive Vehicle Dynamics Models

In this paper, a review is presented on automotive vehicle dynamics modeling. Applied vehicle dynamics models from various application fields are analyzed and classified in the first section. Vehicle dynamics models may be simplified because of different reasons: several control/estimation/analysis methods are suitable only for simplified models (e.g. using control-oriented models), or because of the computational cost. Detailed/truth models of vehicle dynamics represent another field of vehicle dynamics modeling, these models play an important role in the virtual prototyping of vehicles. In the second section, the main modeling considerations of vehicle dynamics are presented in longitudinal, lateral and vertical directions. Various physical effects must be considered in the case of vehicle dynamics modeling, a lot of these effects are significant only in a specific direction of the vehicle body, which is the main potential of model simplification. The section presents vehicle modeling considerations in all of the three translational directions of the vehicle body

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

Holistic Temporal Situation Interpretation for Traffic Participant Prediction

For a profound understanding of traffic situations including a prediction of traf- fic participants’ future motion, behaviors and routes it is crucial to incorporate all available environmental observations. The presence of sensor noise and depen- dency uncertainties, the variety of available sensor data, the complexity of large traffic scenes and the large number of different estimation tasks with diverging requirements require a general method that gives a robust foundation for the de- velopment of estimation applications. In this work, a general description language, called Object-Oriented Factor Graph Modeling Language (OOFGML), is proposed, that unifies formulation of esti- mation tasks from the application-oriented problem description via the choice of variable and probability distribution representation through to the inference method definition in implementation. The different language properties are dis- cussed theoretically using abstract examples. The derivation of explicit application examples is shown for the automated driv- ing domain. A domain-specific ontology is defined which forms the basis for four exemplary applications covering the broad spectrum of estimation tasks in this domain: Basic temporal filtering, ego vehicle localization using advanced interpretations of perceived objects, road layout perception utilizing inter-object dependencies and finally highly integrated route, behavior and motion estima- tion to predict traffic participant’s future actions. All applications are evaluated as proof of concept and provide an example of how their class of estimation tasks can be represented using the proposed language. The language serves as a com- mon basis and opens a new field for further research towards holistic solutions for automated driving

Performance of Sensor Fusion for Vehicular Applications

Sensor fusion is a key system in Advanced Driver Assistance Systems, ADAS. The perfor-mance of the sensor fusion depends on many factors such as the sensors used, the kinematicmodel used in the Extended Kalman Filter, EKF, the motion of the vehicles, the type ofroad, the density of vehicles, and the gating methods. The interactions between parametersand the extent to which individual parameters contribute to the overall accuracy of a sensorfusion system can be difficult to assess.In this study, a full-factorial experimental evaluation of a sensor fusion system basedon a real vehicle was performed. The experimental results for different driving scenariosand parameters are discussed and the factors that make the most impact are identified.The performance of sensor fusion depends on many factors such as the sensors used, thekinematic model used in the Extended Kalman Filter (EKF) motion of the vehicles, type ofroad, density of vehicles, and gating methods.This study identified that the distance between the vehicles has the largest impact on theestimation error because the vision sensor performs poorly with increased distance. In addi-tion, it was identified that the kinematic models had no significant impact on the estimation.Last but not least, the ellipsoid gates performed better than rectangular gates.In addition, we propose a new gating algorithm called an angular gate. This algorithmis based on the observation that the data for each target lies in the direction of that target.Therefore, the angle and the range can be used for setting up a two-level gating approachthat is both more intuitive and computationally faster than ellipsoid gates. The angulargates can achieve a speedup factor of up to 2.27 compared to ellipsoid gates.Furthermore, we provide time complexity analysis of angular gates, ellipsoid gates, andrectangular gates demonstrating the theoretical reasons why angular gates perform better.Last, we evaluated the performance of the Munkres algorithm using a full factorial designand identified that narrower gates can speedup the running time of the Munkres algorithmand, surprisingly, even improve the RMSE in some cases.The low target maneuvering index of vehicular systems was identified as the reason whythe kinematic models do not have an impact on the estimation. This finding supports the useof simpler and computationally inexpensive filters instead of complex Interacting MultipleModel filters. The angular gates also improve the computational efficiency of the overallsensor fusion system making them suitable for vehicular application as well as for embeddedsystems and robotics

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Trajectory generation for lane-change maneuver of autonomous vehicles

Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during lane-change maneuvers. ^ We first propose a two-stage, interactive-multiple-model-based estimator to perform multi-target tracking of neighboring vehicles in a lane-changing scenario. The first stage deals with an adaptive window based turn-rate estimation for tracking maneuvering target vehicles using Kalman filter. In the second stage, variable-structure models with updated estimated turn-rate are utilized to perform data association followed by velocity estimation. Based on the estimated velocities of neighboring vehicles, piecewise Bezier-curve-based methods that minimize the safety/collision risk involved and maximize the comfort ride have been developed for the generation of desired trajectory for lane-change maneuvers. The proposed velocity-estimation and trajectory-generation algorithms have been validated experimentally using Pioneer3- DX mobile robots in a simulated lane-change environment as well as validated by computer simulations