scholarly journals An Autonomous Vehicle Control Stack

10.29007/r5n9 ◽  
2018 ◽  
Author(s):  
Alena Rodionova ◽  
Matthew O'Kelly ◽  
Houssam Abbas ◽  
Vincent Pacelli ◽  
Rahul Mangharam
Keyword(s):  
Free Space ◽  
Adaptive Cruise Control ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Bench Mark ◽  
High Fidelity ◽  
Cruise Control ◽  
Nonlinear Odes ◽  
Control Subsystem ◽  
Testing Tools

This benchmark presents an implementation of a standard control stack for an Autonomous Vehicle (AV). The control stack is made up of a behavioral planner (providing waypoints for the AV to visit in sequence), a trajectory planner (which computes smooth trajectory that the AV should follow to go between waypoints) and a trajectory tracker (which actuates the AV to make it follow the planned trajectory as closely as possible). The behavioral planner is purposefully simple, while the trajectory planner is a a high-fidelity approximation of a planner that was tested on a real Prius, and the tracker was validated by others in previous work on a real Cadillac SRX. The interest of this benchmark is that it includes all three components, rather than one AV control subsystem (such as only adaptive cruise control or only a lane-keeper), and the planners are significantly more realistic than most existing benchmarks or models. It can be used as a baseline AV system for verification and testing tools, which must be able to handle at least the complexity of this controller. This includes simple choices made by the behavioral planner when the current waypoint cannot be reached, discrete and continuous nonlinear optimizations solved by the trajectory planner, and nonlinear ODEs solved by the trajectory tracker. The bench- mark comes with three road topologies: a free space with obstacles, a curved road, and a roundabout.

Consumer Acceptance of Adaptive Cruise Control following Experience with a Prototype System

1995 ◽  
Vol 39 (17) ◽  
pp. 1092-1096 ◽  
Author(s):  
James R. Sayer ◽  
Mary Lynn Mefford ◽  
Paul S. Fancher
Keyword(s):  
Focus Groups ◽  
Control Systems ◽  
System Reliability ◽  
Adaptive Cruise Control ◽  
Vehicle Control ◽  
Ease Of Use ◽  
Consumer Acceptance ◽  
Prototype System ◽  
Cruise Control ◽  
Technology System

Reactions to adaptive cruise control (ACC) were solicited from drivers following use of an ACC equipped vehicle for one hour in an actual highway environment. Participant's impressions were obtained through questionnaires, administered immediately following the exposure, and later in focus groups. Individuals of varying age and conventional cruise control usage took part in the study. The issues of comfort, safety, ease-of-use, and estimated worth were addressed. While participants offered favorable responses towards ACC, despite having limited safety concerns, they were willing to pay surprisingly little for the added convenience provided. The issues of driver over-dependency on technology, system reliability, and customized features appear to warrant additional investigation to overcome consumer's hesitation towards purchasing and using ACC and similar forms of advanced vehicle control systems.

Effect of Vehicle Control Format on Driver Performance and Attention Allocation under Adaptive Cruise Control

2018 ◽  
Vol 62 (1) ◽  
pp. 1510-1514
Author(s):  
Yulin Deng ◽  
David Kaber
Keyword(s):  
Task Performance ◽  
Secondary Task ◽  
Adaptive Cruise Control ◽  
Driving Simulator ◽  
Touch Screen ◽  
Vehicle Control ◽  
Longitudinal Motion ◽  
Performance Study ◽  
Cruise Control ◽  
Study Results

Nowadays many major automobile manufacturers have implemented multiple novel control formats along with traditional manual controls in their vehicle models, as revealed by a vehicle survey. This study conducted a driving simulator-based assessment of driver visual behavior and performance in use of different vehicle control interfaces, while using adaptive cruise control (ACC; i.e., an automated assistance system controlling longitudinal motion of the vehicle). Findings suggest that touch screen controls lead to greater visual workload and degraded secondary task performance. Study results also demonstrated that redundancy of control formats (the combination of touch screen and manual controls) degrades secondary task performance. Results of this research are expected to provide applicable guidance for in-vehicle control format design.

Modeling and Characterization of Driving Styles for Adaptive Cruise Control in Personalized Autonomous Vehicles

2017 ◽  
Author(s):  
Drew Bolduc ◽  
Longxiang Guo ◽  
Yunyi Jia
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Research Effort ◽  
Autonomous Vehicle ◽  
Individual Preference ◽  
Driver Model ◽  
Cruise Control ◽  
Critical Function ◽  
Driving Style ◽  
Safety And Reliability

For autonomous vehicles to gain widespread customer acceptance, safety and reliability are not nearly enough. Comfort and familiarity of the ride is also of essential importance. Because these are highly subjective factors, autonomous vehicles must be able to adopt personal driving styles to meet individual preference. The adaptive cruise control (ACC) system is a critical function performed by the autonomous vehicle and much research effort has been devoted to the development of a system that acts as a human driver. However, studies which investigate ACC models capable of learning a driving style are limited. In this paper, we propose a method to extract quantifiable parameters which represent a drivers’ driving style and apply these parameters to personalize the longitudinal control of an autonomous vehicle. We then develop a longitudinal driver model that integrates those parameters to enable the ACC system to mimic the driving style of the driver. Finally, the effectiveness of the extraction method and the driver model are obtained through simulation.

Influence of Speed Forecasting on the Performance of Ecological Adaptive Cruise Control

2019 ◽  
Author(s):  
Eunjeong Hyeon ◽  
Youngki Kim ◽  
Niket Prakash ◽  
Anna G. Stefanopoulou
Keyword(s):  
Fuel Economy ◽  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Autonomous Vehicle ◽  
Cruise Control ◽  
Prediction Horizon ◽  
The Future ◽  
V2v Communications ◽  
Urban Conditions ◽  
Lead Vehicle

Abstract In congested urban conditions, the fuel economy of a vehicle can be highly affected by traffic flow and particularly, the immediately preceding (lead) vehicle. Thus, estimating the future trajectories of the lead vehicle is essential to optimize the following vehicle’s maneuvers for its fuel economy. This paper investigates the influence of speed forecasting on the performance of an ecological adaptive cruise control (eco-ACC) strategy for connected autonomous vehicles. The real-time speed predictor proposed in [1] is applied to forecast the future speed profiles of the lead vehicle over a short prediction horizon. Under the assumption that vehicle-to-vehicle (V2V) communications are available, V2V information from multiple lead vehicles is utilized in the prediction process. Eco-ACC is formulated in a model predictive control (MPC) framework to control the connected autonomous vehicle. The influence of the state prediction to the performance of eco-ACC in terms of fuel economy and acceleration is evaluated with different number of connected vehicles.

Smart Driving System for Improving Traffic Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 236
Author(s):  
Rajesh Kumar Gupta ◽  
L. N. Padhy ◽  
Sanjay Kumar Padhi
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Urban Areas ◽  
Adaptive Cruise Control ◽  
Human Perception ◽  
Cruise Control ◽  
Driving System ◽  
V2v Communication ◽  
Traffic Conditions ◽  
Cooperative Adaptive Cruise Control

Traffic congestion on road networks is one of the most significant problems that is faced in almost all urban areas. Driving under traffic congestion compels frequent idling, acceleration, and braking, which increase energy consumption and wear and tear on vehicles. By efficiently maneuvering vehicles, traffic flow can be improved. An Adaptive Cruise Control (ACC) system in a car automatically detects its leading vehicle and adjusts the headway by using both the throttle and the brake. Conventional ACC systems are not suitable in congested traffic conditions due to their response delay.  For this purpose, development of smart technologies that contribute to improved traffic flow, throughput and safety is needed. In today’s traffic, to achieve the safe inter-vehicle distance, improve safety, avoid congestion and the limited human perception of traffic conditions and human reaction characteristics constrains should be analyzed. In addition, erroneous human driving conditions may generate shockwaves in addition which causes traffic flow instabilities. In this paper to achieve inter-vehicle distance and improved throughput, we consider Cooperative Adaptive Cruise Control (CACC) system. CACC is then implemented in Smart Driving System. For better Performance, wireless communication is used to exchange Information of individual vehicle. By introducing vehicle to vehicle (V2V) communication and vehicle to roadside infrastructure (V2R) communications, the vehicle gets information not only from its previous and following vehicle but also from the vehicles in front of the previous Vehicle and following vehicle. This enables a vehicle to follow its predecessor at a closer distance under tighter control.

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