Vehicle dynamics

About: Vehicle dynamics is a research topic. Over the lifetime, 12909 publications have been published within this topic receiving 204091 citations.

Efficient Two-phase 3D Motion Planning for Small Fixed-wing UAVs

Abstract: We present an efficient two-phase approach to motion planning for small fixed-wing unmanned aerial vehicles (UAVs) navigating in complex 3D air slalom environments. A coarse global motion planner first computes a kinematically feasible obstacle-free path in a discretized 3D workspace which roughly satisfies the kinematic constraints of the UAV. Given a coarse global path, a fine local motion planner is used to compute a more accurate trajectory for the UAV at a higher level of detail. The local planner is iterated as the vehicle traverses and refines the global path as needed up to its planning horizon. We also introduce a new planning heuristic for 3D motions of fixed-wing UAVs based on 2D Dubins curves, along with precomputed sets of motion primitives derived from the vehicle dynamics model in order to achieve high efficiency.

Development of a rear-end collision avoidance system with automatic brake control

Abstract: We have studied active safety technologies from the standpoint of “collision avoidance”. This paper describes a rear-end collision avoidance system with automatic brake control, which avoids a collision to the vehicle in front caused by inadvertent human errors using automatic emergency braking. The system is comprised of four key technological elements, headway distance measurement using scanning laser radar, path estimation algorithm with vehicle dynamics, collision prediction to the vehicle in front by a safe/danger decision algorithm, and longitudinal automatic brake control.

A Reinforcement Learning Approach to Autonomous Decision Making of Intelligent Vehicles on Highways

Abstract: Autonomous decision making is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a reinforcement learning approach with value function approximation and feature learning is proposed for autonomous decision making of intelligent vehicles on highways. In the proposed approach, the sequential decision making problem for lane changing and overtaking is modeled as a Markov decision process with multiple goals, including safety, speediness, smoothness, etc. In order to learn optimized policies for autonomous decision-making, a multiobjective approximate policy iteration (MO-API) algorithm is presented. The features for value function approximation are learned in a data-driven way, where sparse kernel-based features or manifold-based features can be constructed based on data samples. Compared with previous RL algorithms such as multiobjective Q-learning, the MO-API approach uses data-driven feature representation for value and policy approximation so that better learning efficiency can be achieved. A highway simulation environment using a 14 degree-of-freedom vehicle dynamics model was established to generate training data and test the performance of different decision-making methods for intelligent vehicles on highways. The results illustrate the advantages of the proposed MO-API method under different traffic conditions. Furthermore, we also tested the learned decision policy on a real autonomous vehicle to implement overtaking decision and control under normal traffic on highways. The experimental results also demonstrate the effectiveness of the proposed method.

Constraint-based planning and control for safe, semi-autonomous operation of vehicles

Abstract: This paper presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths. This emphasis on constraints facilitates “minimally-invasive” control for human-machine systems; instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. The method evaluates candidate homotopies based on “restrictiveness”, rather than traditional measures of path goodness, and designs and enforces requisite constraints on the human's control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. Identification of these homotopic classes in off-road environments is performed using geometric constructs. The goodness of competing homotopies and their associated constraints is then characterized using geometric heuristics. Finally, input limits satisfying homotopy and vehicle dynamic constraints are enforced using threat-based feedback mechanisms to ensure that the vehicle avoids collisions and instability while preserving the human operator's situational awareness and mental models. The methods developed in this work are shown in simulation and experimentally demonstrated in safe, high-speed teleoperation of an unmanned ground vehicle.

Cornering stiffness estimation based on vehicle lateral dynamics

Abstract: In this article, the cornering stiffness estimation problem based on the vehicle bicycle (one-track) model is studied. Both time-domain and frequency-domain-based methods are analyzed, aiming to estimate the effective cornering stiffness, defined as the ratio between the lateral force and the slip angle at the two axles. Several methods based on the bicycle model were developed, each having specific pros/cons related to practical implementations. The developed algorithms were evaluated on the basis of the simulation data from the bicycle model and the CarSimTM software. Finally, selected algorithms were evaluated using experimental data.