[Paper Review] Hardware-In-the-Loop for Connected Automated Vehicles Testing in Real Traffic

Yeojun Kim, Samuel Tay+3 more|arXiv (Cornell University)|Jul 21, 2019

Real-time simulation and control systems11 references10 citations

TL;DR

This paper presents a hardware-in-the-loop (HIL) simulation platform integrating real vehicle dynamics, traffic microsimulation, and on-board ECUs to test connected automated vehicle (CAV) control and planning algorithms in realistic urban environments. By combining PreScan, Vissim, ETAS DESK-LABCAR, and on-board ECUs with real traffic data, the setup enables repeatable, safe, and interactive testing of model predictive control-based eco-ACC, demonstrating significant energy savings through coasting and minimal braking in urban scenarios with traffic lights and other vehicles.

ABSTRACT

We present a hardware-in-the-loop (HIL) simulation setup for repeatable testing of Connected Automated Vehicles (CAVs) in dynamic, real-world scenarios. Our goal is to test control and planning algorithms and their distributed implementation on the vehicle hardware and, possibly, in the cloud. The HIL setup combines PreScan for perception sensors, road topography, and signalized intersections; Vissim for traffic micro-simulation; ETAS DESK-LABCAR/a dynamometer for vehicle and powertrain dynamics; and on-board electronic control units for CAV real time control. Models of traffic and signalized intersections are driven by real-world measurements. To demonstrate this HIL simulation setup, we test a Model Predictive Control approach for maximizing energy efficiency of CAVs in urban environments.

Motivation & Objective

• To develop a repeatable, safe, and realistic testing environment for connected automated vehicle (CAV) control and planning algorithms in complex urban traffic.
• To overcome limitations of existing HIL setups by integrating high-fidelity vehicle dynamics, real-world traffic microsimulation, and bidirectional interaction between CAVs and surrounding traffic.
• To validate energy-efficient control strategies—specifically eco-ACC—under realistic conditions using real traffic data and physical vehicle hardware.
• To enable distributed control and cloud connectivity in the HIL framework for scalable and extensible CAV algorithm testing.
• To demonstrate the feasibility of using data-driven virtual environments for consistent, repeatable, and safe CAV validation.

Proposed method

• Integrates PreScan for high-fidelity perception, road topology, and signalized intersections with real-world measurements.
• Uses PTV Vissim for data-driven, microscopic traffic microsimulation based on real traffic and signal phase data.
• Employs ETAS DESK-LABCAR as a dynamometer to simulate high-fidelity vehicle and powertrain dynamics using real vehicle measurements.
• Connects on-board ECUs (e.g., dSpace MicroAutoBox, Matrix PC) with the cloud (e.g., AWS) for distributed control and planning execution.
• Enables real-time, bidirectional interaction between the physical CAV and simulated traffic, including V2X communication and signalized intersection behavior.
• Implements a Model Predictive Control (MPC) framework for eco-ACC that minimizes braking and optimizes velocity tracking while respecting traffic light constraints.

Experimental results

Research questions

• RQ1 Can a hardware-in-the-loop platform combining real vehicle dynamics, data-driven traffic simulation, and on-board ECUs enable repeatable and safe testing of CAV control algorithms in real urban environments?
• RQ2 How effectively can an MPC-based eco-ACC controller reduce energy consumption in urban traffic with signalized intersections and interacting vehicles?
• RQ3 To what extent does the HIL setup accurately replicate real-world traffic interactions and vehicle behavior, especially in terms of energy efficiency and safety?
• RQ4 How does the integration of cloud connectivity and distributed control enhance the scalability and realism of CAV testing in the HIL framework?
• RQ5 Can the HIL platform support the validation of CAV controllers under worst-case assumptions (e.g., sudden deceleration of front vehicles) while maintaining safety and efficiency?

Key findings

• The HIL setup successfully enabled real-time, interactive simulation of a CAV reacting to dynamic traffic and signalized intersections using real traffic data.
• The eco-ACC controller achieved energy savings by minimizing braking and using coasting phases, particularly before approaching red lights or decelerating front vehicles.
• In urban scenarios, the ego vehicle maintained safe distances and avoided collisions by applying small braking torques due to model mismatch between high-fidelity and control-oriented dynamics.
• The controller demonstrated safe and stable behavior in complex urban environments with eight traffic signals and multiple interacting vehicles, using only radar and lane detection sensor data.
• The HIL platform enabled consistent, repeatable testing of CAV algorithms under realistic conditions, with results showing that coasting was prioritized over braking to reduce energy waste.
• The integration of cloud-based connectivity and on-board ECUs allowed for scalable, distributed control implementation, supporting future extensions to larger-scale CAV systems.