Search Results

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

Software for autonomous vehicles is highly complex and requires vast amount of vehicle testing to achieve a certain level of confidence in safety, quality and reliability. According to the RAND Corporation, a 100 vehicle fleet running 24 hours a day 365 days a year at a speed of 40 km/hr, would require 17 billion driven kilometers of testing and take 518 years to fully validate the software with 95% confidence such that its failure rate would be 20% better than the current human driver fatality rate [1]. In order to reduce cost and time to accelerate autonomous software development, Hardware-in-the-Loop (HIL) simulation is used to supplement vehicle testing. For autonomous vehicles, path following controls are an integral part for achieving lateral control. Combining the aforementioned concepts, this paper focuses on a real-time implementation of a path-following lateral controller, developed by Freund and Mayr [2].

The automotive industry is heading towards the path of autonomy with the development of autonomous vehicles. An autonomous vehicle consists of two main components. The first is the software which is responsible for the decision-making capabilities of the system. The second is the hardware which encompasses all aspects of the physical vehicle which are responsible for vehicle motion such as the engine, brakes and steering subsystems along with their corresponding controls. This component forms the basis of the autonomous vehicle platform. For SAE Level 4 autonomous vehicles, where an automated driving system is responsible for all the dynamics driving tasks including the fallback driving performance in case of system faults, redundant mechanical systems and controls are required as part of the autonomous vehicle platform since the driver is completely out of the loop with respect to driving.

Due to the increasing concerns on energy and environmental issues, the automotive industry has seen increased growth and development of electric and electrified vehicles [1]. The power-split design is one of the most common drivetrain configurations of a hybrid or electrified vehicle. The propulsion system of a power-split hybrid vehicle typically comprises of an engine drive system in which the engine, drivetrain and generator are mechanically coupled on a planetary gear set driveline while the electric drive system consists of a high voltage battery and a traction motor [2]. In recent years, Hardware-in-the-Loop (HIL) simulation has become an increasingly common approach for controls rapid prototyping and validation as part of the automotive product development cycle [2, 3].

Adaptive Cruise Control (ACC) is becoming a common feature in modern day vehicles with the advancement of Advanced Driver Assist Systems (ADAS). Simultaneously, Hardware-in-the-Loop (HIL) simulation has emerged as a major component of the automotive product development cycle as it can accelerate product development and validation by supplementing in-vehicle testing. Specifically, HIL simulation has become an integral part of the controls development and validation V-cycles by enabling rapid prototyping of control software for Electronic Control Units (ECUs). Traditionally, ACC algorithms have been validated on a system or subsystem HIL bench with the ACC ECU in the loop such that the HIL bench acts as the host or trailing vehicle with the target or preceding vehicle usually simulated using as an object that follows a pre-defined motion profile.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.

Automotive Applications of Hardware-in-the-Loop (HIL) Simulation shines a light on HIL simulation testing methodology commonly used in the automotive industry for conventional, electrification and autonomy applications and can serve as an introductory resource for college students looking to join the automotive industry or experienced technical professionals who need a deeper understanding on what is HIL simulation, what are its benefits and how can it be used in their respective organizations.