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When commercial vehicles have less than ideal lateral dynamics traveling at high speeds, those dynamics can sometimes be a significant factor in serious and fatal accidents that occur. The primary goal of this study was to create a validated vehicle dynamics model to aid in handling evaluations and a validated model that can play an important role in accurately predicting the movement of the vehicle in limit conditions. The model is used to simulate the effect on roll gradient by altering spring stiffnesses and adding a rear stabilizer for a class 6 or class 7 commercial vehicle (CV). Outputs from 4-post Kinematics & Compliance (K&C) tests of a prototype vehicle were used to model the suspension system. The tire model was developed in collaboration with Calspan [1] using physical tire testing. The vehicle masses, inertias and stiffnesses were modelled using IPG TruckMaker for Simulink (TM4SL) [2].

Exploring an unknown place autonomously is a challenge for robots, especially when the environment is changing. Moreover, in real world application, efficient path planning is crucial for autonomous vehicles to have timely response to execute a collision-free motion. In this paper we focus on environment exploration which enables an automated system to establish a map of an unknown environment with unforeseen objects moving within it. We introduce an exploration package that enables robots self-exploration with an online collision avoidance planner. The package consists of exploration module, global planner module and local planner module. We modularize the package so that developers can easily make modifications or even substitutions to some modules for their specific application. In order to validate the algorithm, we designed and built a robot car as a low cost validation platform to test the autonomous vehicle algorithms in the real world.