2,988 research outputs found
Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments using Robust MPC
Automated vehicles are increasingly getting mainstreamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, overtake, etc.) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway, motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards.
These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: (i) it is free from nonconvex collision avoidance constraints, (ii) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion, and (iii) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for highspeed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment
Trajectory planning for autonomous high-speed overtaking in structured environments using robust MPC
Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment
On Traffic Situation Predictions for Automated Driving of Long Vehicle Combinations
The introduction of longer vehicle combinations for road transports than are currently allowed is an important viable option for achieving the environmental goals on transported goods in Sweden and Europe by the year 2030. This thesis addresses how driver assistance functionality for high-speed manoeuvring can be designed and realized for prospective long vehicle combinations. The main focus is the derivation and usage of traffic situation predictions in order to provide driver support functionalities with a high driver acceptance. The traffic situation predictions are of a tactical character and include a time horizon of up to 10 s.
Data collection of manual and automated driving with an A-double combination was carried out in a moving-base driving simulator. The driving scenario was comprised of a relatively curvy and hilly single-lane Swedish county road (180). The driving trajectories were analysed and complemented with results from optimization. Based on observations of utilized accelerations it was proposed that the combined steering and braking should prioritize a smooth and comfortable driving experience.
It was hypothesized that high driver acceptance of driver assistance functionality including automated steering and propulsion/braking, can be realized by utilizing driver models inspired by human cognition as an integrated part in the generation of traffic situation predictions. A longitudinal and lateral driver model based on optic information was proposed for lane-change manoeuvring.
The driver model was implemented in a real-time framework for automated driving of an A-double combination on a multiple lane one-way road. Simulations showed that the framework gave reasonable results for maintain lane and lane change manoeuvres at constant and varying longitudinal velocities
Real-time motion planning methods for autonomous on-road driving: state-of-the-art and future research directions
Currently autonomous or self-driving vehicles are at the heart of academia and industry research because of its multi-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature. The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
Open access articleCurrently autonomous or self-driving vehicles are at the heart of academia and industry
research because of its multi-faceted advantages that includes improved safety, reduced congestion,lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a given origin to a given destination, motion planning methods incorporate searching for a path to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have been proposed in the literature.
The purpose of this paper is to review existing approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1) finding a path, (2) searching for the safest manoeuvre and (3) determining the most feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existing methods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performance of planning algorithms at all three levels. Some promising areas of future focus have been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing
technologies that are essential for planning with the aim of reducing the total cost of
driverless vehicles. This critical review on planning techniques presented in this paper, along with the associated discussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research
Autonomous Collision Avoidance Using MPC with LQR-Based Weight Transformation
Model predictive control (MPC) is a multi-objective control technique that can handle system constraints. However, the performance of an MPC controller highly relies on a proper prioritization weight for each objective, which highlights the need for a precise weight tuning technique. In this paper, we propose an analytical tuning technique by matching the MPC controller performance with the performance of a linear quadratic regulator (LQR) controller. The proposed methodology derives the transformation of a LQR weighting matrix with a fixed weighting factor using a discrete algebraic Riccati equation (DARE) and designs an MPC controller using the idea of a discrete time linear quadratic tracking problem (LQT) in the presence of constraints. The proposed methodology ensures optimal performance between unconstrained MPC and LQR controllers and provides a sub-optimal solution while the constraints are active during transient operations. The resulting MPC behaves as the discrete time LQR by selecting an appropriate weighting matrix in the MPC control problem and ensures the asymptotic stability of the system. In this paper, the effectiveness of the proposed technique is investigated in the application of a novel vehicle collision avoidance system that is designed in the form of linear inequality constraints within MPC. The simulation results confirm the potency of the proposed MPC control technique in performing a safe, feasible and collision-free path while respecting the inputs, states and collision avoidance constraints
Lane-Change Initiation and Planning Approach for Highly Automated Driving on Freeways
Quantifying and encoding occupants' preferences as an objective function for
the tactical decision making of autonomous vehicles is a challenging task. This
paper presents a low-complexity approach for lane-change initiation and
planning to facilitate highly automated driving on freeways. Conditions under
which human drivers find different manoeuvres desirable are learned from
naturalistic driving data, eliminating the need for an engineered objective
function and incorporation of expert knowledge in form of rules. Motion
planning is formulated as a finite-horizon optimisation problem with safety
constraints. It is shown that the decision model can replicate human drivers'
discretionary lane-change decisions with up to 92% accuracy. Further proof of
concept simulation of an overtaking manoeuvre is shown, whereby the actions of
the simulated vehicle are logged while the dynamic environment evolves as per
ground truth data recordings.Comment: 6 pages, 8 figures, The 2020 IEEE 92nd Vehicular Technology
Conferenc
Automated driving and autonomous functions on road vehicles
In recent years, road vehicle automation has become an important and popular topic for research
and development in both academic and industrial spheres. New developments received
extensive coverage in the popular press, and it may be said that the topic has captured the
public imagination. Indeed, the topic has generated interest across a wide range of academic,
industry and governmental communities, well beyond vehicle engineering; these include computer
science, transportation, urban planning, legal, social science and psychology. While this
follows a similar surge of interest – and subsequent hiatus – of Automated Highway Systems
in the 1990’s, the current level of interest is substantially greater, and current expectations
are high. It is common to frame the new technologies under the banner of “self-driving cars”
– robotic systems potentially taking over the entire role of the human driver, a capability that
does not fully exist at present. However, this single vision leads one to ignore the existing
range of automated systems that are both feasible and useful. Recent developments are underpinned
by substantial and long-term trends in “computerisation” of the automobile, with
developments in sensors, actuators and control technologies to spur the new developments in
both industry and academia. In this paper we review the evolution of the intelligent vehicle
and the supporting technologies with a focus on the progress and key challenges for vehicle
system dynamics. A number of relevant themes around driving automation are explored in
this article, with special focus on those most relevant to the underlying vehicle system dynamics.
One conclusion is that increased precision is needed in sensing and controlling vehicle
motions, a trend that can mimic that of the aerospace industry, and similarly benefit from
increased use of redundant by-wire actuators
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