Using scaled vehicles to investigate the influence of various properties on rollover propensity

Travis, William; Whitehead, Randy; Bevly, David M.; Flowers, George T.

doi:10.23919/acc.2004.1384431

Cited by 22 publications

(8 citation statements)

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“…A framework with multi-fidelity simulation and accompanying hardware platform for use in reinforcement learning problems relating to autonomous driving was presented in [12]. A 1:5 scale autonomous platform was developed to investigate stability control in [13], [14]. While these platforms were successfully used for the experiments in their respective publications, there is not enough public information available to be able to build, operate, and program one without essentially starting from scratch.…”

Section: Platformmentioning

confidence: 99%

“…Scaled vehicles ranging in size from 1:16 to 1:5 the size of an actual vehicle, often based on radio controlled (RC) vehicles, are easier and less expensive to operate than a full-sized platform. Despite recent progress and many publications detailing scaled autonomous testbeds [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], much of the available results lack reproducibility because of the one-off nature of these testbeds, restrictions imposed by the use of private datasets, and inconsistent testing methods. Inconsistency is an especially critical problem, as many researchers should be able to test and compare potential algorithms under the same conditions and platforms in order to be able to obtain meaningful comparisons and advance the science of high-speed autonomy.…”

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confidence: 99%

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AutoRally: An Open Platform for Aggressive Autonomous Driving

et al. 2019

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Scaled platforms constructed from modified RC cars are popular in the academic and hobby communities. These platforms are typically 0.2 m to 1 m long and weigh between 1 kg and 25 kg. Costs range from a few hundred to tens of thousands of dollars, largely determined by the size, sensors, and computing. Construction, maintenance, and programming is typically handled by a small team of students or researchers. Recently, several open source projects released complete documentation and interface software, which is in contrast to the one-off nature of older work that often lacked enough information to replicate. Documentation for open source platforms normally includes parts lists, build instructions, 3 and interface software for the sensors and actuators. Availability of tutorials, simulation environments, and public datasets vary by project. Common sensors include wheel speed, inertial measurement unit (IMU), cameras, depth sensors, ultrasonic, and light detection and ranging (Lidar) units. The target environment for these platforms is typically indoors on a smooth surface. The Donkey Car [5] is an easy to build 1:16 scale autonomous platform for the DIY Roborace events targeted at hobbyists. Onboard computing and sensing are a Raspberry Pi 3 with a matching wide angle camera. The Berkeley Autonomous Race Car (BARC) [6] is a 1:10 scale vehicle designed as a simple and affordable research platform for self-driving vehicle technologies that has been successfully used to demonstrate various control algorithms. The onboard ODROID-XU4 is similar in computational performance to the Raspberry Pi 3, and the sensor suite includes a hobby grade camera, IMU, four ultrasonic range finders, and Hall effect wheel speed sensors. The F1/10 project [7] and accompanying Autonomous Racing Competition allows teams to race against one another using a common 1:10 scale platform developed at the University of Pennsylvania. Computing on the F1/10 platform is performed by an Nvidia Jetson. The sensor suite includes a hobby IMU, compact indoor Hokuyo 2D Lidar, and optional Structure and Zed depth and motion sensing cameras. The 1:10 scale Rapid Autonomous Complex-Environment Competing Ackermann-steering Robot (RACECAR) [8] from Massachusetts Institute of Technology is a platform for researchers creating applications for self driving cars. RACECAR also uses the Nvidia Jetson for computing, and includes the same Hokuyo Lidar and Zed stereo camera as the F1/10 platform. Table 1 provides a comparison of these open source scaled platforms.

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Section: Platformmentioning

confidence: 99%

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confidence: 99%

AutoRally: An Open Platform for Aggressive Autonomous Driving

et al. 2019

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“…The HiL (hardware-in-the-loop) simulations can offer a degree of feedback from individual components of the real system (Schuette & Waeltermann, 2005). Scaled vehicles have been used especially in universities to investigate various vehicle control systems performance and validate models: rollover propensity (Travis et al, 2004), backing-up driver assistance (Chiu & Goswami, 2012), vehicle stability control (Lapapong et al, 2009), anti-lock brake actuation and control (Patil & Longoria, 2007). Simulation and use of scalar vehicles are preferred due to lower costs and development times, but they do not ensure subjective (human) feedback on the dynamic behavior.…”

Section: Introductionmentioning

confidence: 99%

Cost effective teaching and research tools for automotive dynamics

Băţăuş

Stoica

Oprean

2017

Balkan Region Conference on Engineering and Business Education

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The paper examines the problem of developing cost effective teaching and research tools for automotive dynamics. Different available solutions are presented and the need of human feedback is emphasized. Based on the demands from industry and academia (benchmarking, virtual prototyping, comparative testing of alternative technologies, development and tuning of the automotive control etc.) a solution is proposed. At the core of the proposed tool is a H2iL (humanand-hardware-in-the-loop) simulator based on an electric vehicle. An architecture is elaborated for the simulator and the proof of concept is done in three steps.

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“…Therefore, a vehicle dynamics prediction system needs a mathematic model and current vehicle state values [1–6]. There are two concerns regarding the mathematic model.…”

Section: Introductionmentioning

confidence: 99%

“…There are two concerns regarding the mathematic model. First, many reports employ simplified vehicle models in the dynamics prediction, such as 2 DOF model [1], 4 DOF [2], and 2 DOF yaw-roll model [1–3,6]. The prediction results may only be acceptable for limited purposes and driving conditions.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

Hsu

Chen

2012

Sensors

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This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

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Using scaled vehicles to investigate the influence of various properties on rollover propensity

Cited by 22 publications

References 13 publications

AutoRally: An Open Platform for Aggressive Autonomous Driving

AutoRally: An Open Platform for Aggressive Autonomous Driving

Cost effective teaching and research tools for automotive dynamics

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

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