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Autonomous vehicle trajectory tracking control is challenged by situations of varying road surface friction, especially in the scenario where there is a sudden decrease in friction in an area with high road curvature. If the situation is unknown to the control law, vehicles with high speed are more likely to lose tracking performance and/or stability, resulting in loss of control or the vehicle departing the lane unexpectedly. However, with connectivity either to other vehicles, infrastructure, or cloud services, vehicles may have access to upcoming roadway information, particularly the friction and curvature in the road path ahead. This paper introduces a model-based predictive trajectory-tracking control structure using the previewed knowledge of path curvature and road friction. In the structure, path following and vehicle stabilization are incorporated through a model predictive controller. Meanwhile, long-range vehicle speed planning and tracking control are integrated to ensure the vehicle can slow down appropriately before encountering hazardous road conditions. This approach has two major advantages. First, the prior knowledge of the desired path is explicitly incorporated into the computation of control inputs. Second, the combined transmission of longitudinal and lateral tire forces is considered in the controller to avoid violation of tire force limits while keeping performance and stability guarantees. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a sharply curving road with varying friction conditions, with results showing that the controller can drive a vehicle up to the handling limits and track the desired trajectory accurately.
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Analytical Longitudinal Speed Planning for CAVs with Previewed Road Geometry and Friction Constraints
Due to the lack of information, current vehicle control systems generally assume that the road friction conditions ahead of a vehicle are unchanged relative to those at the vehicle's current position. This can result in dangerous situations if the friction is suddenly decreasing from the current situation, or overly conservative driving styles if the friction of the current situation is worse than the roadway ahead. However, with connectivity either to other vehicles, infrastructure, or cloud services, future vehicles may have access to upcoming roadway information; this is particularly valuable for planning velocity trajectories that consider the friction and geometry in the road path ahead. This paper introduces a method for planning longitudinal speed profiles for Connected and Autonomous Vehicles (CAVs) that have previewed information about path geometry and friction conditions. The novelty of this approach is to explicitly include consideration of the friction ellipse available along the intended path. The paper derives an analytical solution for certain preview cases that upper-bounds the allowable vehicle velocity profile while preventing departure from the friction ellipse. The results further define the relationship between a lower bound on friction, the path geometry, and minimum friction preview distance. This relationship is used to ensure the vehicle has sufficient time to take action for upcoming hazardous situations. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a curving road with changing friction conditions, with results showing that, with sufficient preview, the vehicle could anticipate allowable and stable path keeping speed.
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- Award ID(s):
- 1932509
- PAR ID:
- 10395538
- Date Published:
- Journal Name:
- 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
- Page Range / eLocation ID:
- 1610 to 1615
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Vehicles can easily lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. In addition, through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space, a friction value, and a confidence interval within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface, where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs.more » « less
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Vehicles are highly likely to lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. And through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space and a friction value within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs. By defining a cloud-based data sharing method for the networks of CAVs, this road friction mapping strategy provides great potential for improving CAVs' control performance and stability via database-mediated feedback systems.more » « less
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Speed planning in a vehicle-following scenario can reduce vehicle fuel consumption even under limited traffic preview and in moderate penetration of connected autonomous vehicles (CAVs), but could also lead to colder exhaust temperature, and consequently, less efficient aftertreatment conversion. To investigate this potential trade-off, this paper presents a model predictive controller (MPC) to optimally plan in an energy-conscious way the optimal speed trajectory for a diesel car following a hypothetical lead vehicle that drives through the velocity trace of a federal test procedure. Using this energy-conscious optimal speed plan we investigate different horizons for three objective functions, including minimum acceleration, minimum fuel consumption and minimum power. Then, MPC results are compared to the trajectories obtained by dynamic programming with full knowledge of the drive cycle. As expected, longer previews lead to smoother velocity trajectories that reduce the fuel consumption by 11% when power is the objective function, if the preview is accurate. When the minimum fuel is set as the objective in the MPC, the controller coordinates to operate the engine at more efficient conditions, which increases the fuel saving to 25%. However, the extra fuel saving is shown to be achieved at the expense of high vehicle NOx emissions, since the engine operates at low speeds and high loads, where the output NOx emissions are high, when the aftertreatment catalyst is not hot enough. Finally, it is shown that the minimum power formulation leads to a better trade-off, where fuel economy can be increased without a large penalty on NOx emissions.more » « less
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ObjectiveThe aim of this study is to measure drivers’ attention to preview and their velocity and acceleration tracking error to evaluate two- and three-dimensional displays for following a winding roadway. BackgroundDisplay perturbation techniques and Fourier analysis of steering movements can be used to infer drivers’ spatio-temporal distribution of attention to preview. Fourier analysis of tracking error time histories provides measures of position, velocity, and acceleration error. MethodParticipants tracked a winding roadway with 1 s of preview in low-fidelity driving simulations. Position and rate-aided vehicle dynamics were paired with top-down and windshield displays of the roadway. ResultsFor both vehicle dynamics, tracking was smoother with the windshield display. This display emphasizes nearer preview positions and has a closer correspondence to the control-theoretic optimal attentional distributions for these tasks than the top-down display. This correspondence is interpreted as a form of stimulus–response compatibility. The position error and attentional signal-to-noise ratios did not differ between the two displays with position control, but with more complex rate-aided control much higher position error and much lower attentional signal-to-noise ratios occurred with the top-down display. ConclusionDisplay-driven influences on the distribution of attention may facilitate tracking with preview when they are similar to optimal attentional distributions derived from control theory. ApplicationDisplay perturbation techniques can be used to assess spatially distributed attention to evaluate displays and secondary tasks in the context of driving. This methodology can supplement eye movement measurements to determine what information is guiding drivers’ actions.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ITSC48978.2021.9564602
