Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

2010 ◽  
Author(s):  
Yiqi Gao ◽  
Theresa Lin ◽  
Francesco Borrelli ◽  
Eric Tseng ◽  
Davor Hrovat
Keyword(s):  
Obstacle Avoidance ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Ground Vehicles ◽  
Vehicle Model ◽  
The Road ◽  
On Line ◽  
Nonlinear Vehicle Model ◽  
On The Road ◽  
High Level

Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.

On the Road to Computer Assisted Compositions

1981 ◽  
Vol 10 (2) ◽  
pp. 133-148 ◽  
Author(s):  
Earl Woodruff ◽  
Carl Bereiter ◽  
Marlene Scardamalia
Keyword(s):  
Qualitative Analysis ◽  
School Age Children ◽  
Computer Assisted ◽  
School Age ◽  
Structural Elements ◽  
The Road ◽  
Composing Process ◽  
On Line ◽  
On The Road ◽  
High Level

Two studies are reported that explore the feasibility of computer assisted composition in helping school-age children handle high-level aspects of the composing process. The first study used a program featuring help in selecting structural elements to include in opinion essays. The twelve grade six students, serving as subjects in the study, reported that the program was helpful, but a qualitative analysis of their products suggests the intervention was too easily assimilated to a low-level “What next?” composing strategy. In an attempt to strengthen the intervention, the second study introduced a response-sensitive questioning procedure. Qualitative measures suggest the thirty-six grade eight students found this on-line intervention to be too intrusive. The two approaches to on-line facilitation are discussed, and lines for the future investigation of computer assisted composition for the novice composer are suggested.

scholarly journals A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 744
Author(s):  
Jorge Godoy ◽  
Víctor Jiménez ◽  
Antonio Artuñedo ◽  
Jorge Villagra
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicles ◽  
Line Of Sight ◽  
The Road ◽  
Occupancy Grid ◽  
One Step ◽  
On The Road ◽  
High Level ◽  
Share Information ◽  
Grid Based

Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

scholarly journals Brake systems: a mind of their own

2021 ◽  
pp. 27-34
Author(s):  
Michael Ellims
Keyword(s):  
Failure Modes ◽  
Electronic Control ◽  
Hydraulic Systems ◽  
Control Software ◽  
The Road ◽  
Current Production ◽  
On The Road ◽  
High Level ◽  
Electronic Sensors ◽  
Systems Failure

Brake systems fitted to current production vehicles are not the relativity straightforward hydraulic systems that many people expect. Rather they have evolved into complex systems which are on their own deliberately capable of affecting the behaviour of a vehicle. Crucially they depend on computers, software and electronic sensors to allow them to form a model of how the vehicle is expected to behave on the road and how it is actually behaving. Like any artefact they can, and do fail. This paper provides a high-level overview of the braking systems currently in place, how these systems act and present some examples of how they have failed in practice. Index words: vehicles; vehicle electronics; electronic control; software; brake systems; failure modes

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

V.—The Occurrence of Glacial Clay on the Cotteswold Plateau

1905 ◽  
Vol 2 (5) ◽  
pp. 216-219 ◽  
Author(s):  
C. Callaway
Keyword(s):  
The Road ◽  
Glacial Clay ◽  
On The Road ◽  
High Level

Mr. L. Richardson, F.G.S., having informed me that he had seen gravels at a high level on the road leading from Stow-on-the-Wold to Burford, I visited the locality, accompanied by Mr. J. W. Gray, F.G.S. About 3⅓ miles north of Burford, at the corner of the turning to Tangley, we came upon a deposit of clay with northern erratics in a quarry of oolite. As the position of such a formation was unexpected, and might be important, it seems desirable to record the discovery.

scholarly journals Privacy-Preserving Billing Scheme against Free-Riders for Wireless Charging Electric Vehicles

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Xingwen Zhao ◽  
Jiaping Lin ◽  
Hui Li
Keyword(s):  
Electric Vehicle ◽  
Electric Energy ◽  
Privacy Preserving ◽  
Sequential Data ◽  
Wireless Charging ◽  
Free Riders ◽  
The Road ◽  
On Line ◽  
5G Systems ◽  
On The Road

Recently, scientists in South Korea developed on-line electric vehicle (OLEV), which is a kind of electric vehicle that can be charged wirelessly while it is moving on the road. The battery in the vehicle can absorb electric energy from the power transmitters buried under the road without any contact with them. Several billing schemes have been presented to offer privacy-preserving billing for OLEV owners. However, they did not consider the existence of free-riders. When some vehicles are being charged after showing the tokens, vehicles that are running ahead or behind can switch on their systems and drive closely for a free charging. We describe a billing scheme against free-riders by using several cryptographic tools. Each vehicle should authenticate with a compensation-prepaid token before it can drive on the wireless-charging-enabled road. The service provider can obtain compensation if it can prove that certain vehicle is a free-rider. Our scheme is privacy-preserving so the charging will not disclose the locations and routine routes of each vehicle. In fact, our scheme is a fast authentication scheme that anonymously authenticates each user on accessing a sequence of services. Thus, it can be applied to sequential data delivering services in future 5G systems.

Coordinated Model Predictive Control on Multi-Lane Roads

2019 ◽  
Author(s):  
Nathan Goulet ◽  
Beshah Ayalew
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Heterogeneous Traffic ◽  
Road Transportation ◽  
The Road ◽  
Energy Saving Potential ◽  
Coordination Scheme ◽  
Predictive Controller

Abstract There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

Analysis of Preference for Autonomous Driving Under Different Traffic Conditions Using a Driving Simulator

2015 ◽  
Vol 27 (6) ◽  
pp. 660-670 ◽  
Author(s):  
Udara Eshan Manawadu ◽  
◽  
Masaaki Ishikawa ◽  
Mitsuhiro Kamezaki ◽  
Shigeki Sugano ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Driving ◽  
Driving Experience ◽  
The Road ◽  
Traffic Conditions ◽  
Passenger Vehicles ◽  
New Type ◽  
On The Road ◽  
Near Future

<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/08.jpg"" width=""300"" /> Driving simulator</div>Intelligent passenger vehicles with autonomous capabilities will be commonplace on our roads in the near future. These vehicles will reshape the existing relationship between the driver and vehicle. Therefore, to create a new type of rewarding relationship, it is important to analyze when drivers prefer autonomous vehicles to manually-driven (conventional) vehicles. This paper documents a driving simulator-based study conducted to identify the preferences and individual driving experiences of novice and experienced drivers of autonomous and conventional vehicles under different traffic and road conditions. We first developed a simplified driving simulator that could connect to different driver-vehicle interfaces (DVI). We then created virtual environments consisting of scenarios and events that drivers encounter in real-world driving, and we implemented fully autonomous driving. We then conducted experiments to clarify how the autonomous driving experience differed for the two groups. The results showed that experienced drivers opt for conventional driving overall, mainly due to the flexibility and driving pleasure it offers, while novices tend to prefer autonomous driving due to its inherent ease and safety. A further analysis indicated that drivers preferred to use both autonomous and conventional driving methods interchangeably, depending on the road and traffic conditions.

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