Race Car Vehicle Dynamics

This paper presents the algorithms and system architecture of an autonomous racecar. The introduced vehicle is powered by a software stack designed for robustness, reliability, and extensibility. In order to autonomously race around a previously unknown track, the proposed solution combines state of the art techniques from different fields of robotics. Specifically, perception, estimation, and control are incorporated into one high-performance autonomous racecar. This complex robotic system, developed by AMZ Driverless and ETH Zurich, finished 1st overall at each competition we attended: Formula Student Germany 2017, Formula Student Italy 2018 and Formula Student Germany 2018. We discuss the findings and learnings from these competitions and present an experimental evaluation of each module of our solution.

AMZ Driverless: The full autonomous racing system

Vehicle slip angle (VSA) estimation is of paramount importance for connected automated vehicle dynamic control, especially in critical lateral driving scenarios. In this paper, a novel kinematic-model-based VSA estimation method is proposed by fusing information from a global navigation satellite system (GNSS) and an inertial measurement unit (IMU). First, to reject the gravity components induced by the vehicle roll and pitch, a vehicle attitude angle observer based on the square-root cubature Kalman filter (SCKF) is designed to estimate the roll and pitch. A novel feedback mechanism based on the vehicle intrinsic information (the steering angle and wheel speed) for the pitch and roll is designed. Then, the integration of the reverse smoothing and grey prediction is adopted to compensate for the cumulative velocity errors during the relatively low sampling interval of the GNSS. Moreover, the GNSS signal delay has been addressed by an estimation-prediction integrated framework. Finally, the results confirm that the proposed method can estimate the VSA under both the slalom and double lane change (DLC) scenarios.

Automated Vehicle Sideslip Angle Estimation Considering Signal Measurement Characteristic

The rising popularity of self-driving cars has led to the emergence of a new research field in recent years: Autonomous racing. Researchers are developing software and hardware for high-performance race vehicles which aim to operate autonomously on the edge of the vehicle’s limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic, and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods, and approaches used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field, we conclude with a summary of open research challenges that will guide future researchers in this field.

Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

Autonomous car racing is a major challenge in robotics It raises fundamental problems for classical approaches such as planning minimum-time trajectories under uncertain dynamics and controlling the car at the limits of its handling Besides, the requirement of minimizing the lap time, which is a sparse objective, and the difficulty of collecting training data from human experts have also hindered researchers from directly applying learning-based approaches to solve the problem In the present work, we propose a learning-based system for autonomous car racing by leveraging a high-fidelity physical car simulation, a course-progress proxy reward, and deep reinforcement learning We deploy our system in Gran Turismo Sport, a world-leading car simulator known for its realistic physics simulation of different race cars and tracks, which is even used to recruit human race car drivers Our trained policy achieves autonomous racing performance that goes beyond what had been achieved so far by the built-in AI, and, at the same time, outperforms the fastest driver in a dataset of over 50,000 human players

Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning

Vehicle dynamics stability control systems rely on the amount of so-called sideslip angle and yaw rate. As the sideslip angle can be measured directly only with very expensive sensors, its estimati...

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An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements:

Autonomous driving at at-limit handling represents a big challenge due to the nonlinear behaviour of a vehicle. In this paper, a hierarchical control scheme composed of two Nonlinear Model Predicti...

Real-time control for at-limit handling driving on a predefined path

The performance of semi-active differential systems is deeply influenced by the way in which they are actuated. Conventional semi-active systems are actuated by pressure-controlled hydraulic actuat...

Design and testing of an innovative electro-hydraulic actuator for a semi-active differential:

Improved lateral stability performances of modern Electric Vehicles equipped with In-Wheel Motors could be achieved by a proper design of the adopted control strategy. The possibility to independently regulates braking and traction efforts delivered at each wheel can lead to increased handling properties during cornering manoeuvres, especially if performed in degraded adherence operative conditions. In this activity, authors propose a newly ESC lateral stability algorithm, integrated with longitudinal stability controller (EBD and ABS) and implemented on a benchmark vehicle use case model. The assessment of the performances is done through co-simulation activities, imposing to the vehicle specified reference trajectories according to related standards, in order to evaluate the achieved stability improvements.

ESC on In-Wheel Motors Driven Electric Vehicle: Handling and Stability Performances Assessment

Suppressing or limiting the differential action of the differential mechanism is the mostly adopted technique to avoid the skidding of a driving wheel of a vehicle riding on a poorly adherent surface. The devices carrying out this function unbalance the traction force distribution in the differential, generating a yaw torque acting on the vehicle as a secondary effect. If the unbalancing action is electronically controlled, this yaw torque can be used to affect the attitude of car as a torque vectoring technique.In this paper, a purpose built differential is presented and its technical features are highlighted, including the electrohydraulic actuation. Moreover, its torque vectoring capabilities are discussed, basing on the numerical simulation campaign performed deploying this device in a 7 DOFs model of a race car with low ground effect.The results of these simulations are compared with the behavior of the same vehicle equipped with a common passive locking differential, to show that the proposed one and its control logic (which relies on only measurable inputs) are able of improving the handling of the vehicle, in terms of both vehicle stability and linearity with the driver’s inputs. Therefore, this system could be considered as a completion of the common ESC (“Electronic Stability Control) systems to control the vehicle attitude when using the brake system is an inefficient solution.Copyright © 2014 by ASME

Claudio Annicchiarico

Papers

An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements:

Real-time control for at-limit handling driving on a predefined path

Design and testing of an innovative electro-hydraulic actuator for a semi-active differential:

ESC on In-Wheel Motors Driven Electric Vehicle: Handling and Stability Performances Assessment

Design of a Semi Active Differential to Improve the Vehicle Dynamics