722 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
From Specifications to Behavior: Maneuver Verification in a Semantic State Space
To realize a market entry of autonomous vehicles in the foreseeable future,
the behavior planning system will need to abide by the same rules that humans
follow. Product liability cannot be enforced without a proper solution to the
approval trap. In this paper, we define a semantic abstraction of the
continuous space and formalize traffic rules in linear temporal logic (LTL).
Sequences in the semantic state space represent maneuvers a high-level planner
could choose to execute. We check these maneuvers against the formalized
traffic rules using runtime verification. By using the standard model checker
NuSMV, we demonstrate the effectiveness of our approach and provide runtime
properties for the maneuver verification. We show that high-level behavior can
be verified in a semantic state space to fulfill a set of formalized rules,
which could serve as a step towards safety of the intended functionality.Comment: Published at IEEE Intelligent Vehicles Symposium (IV), 201
Legal Decision-making for Highway Automated Driving
Compliance with traffic laws is a fundamental requirement for human drivers
on the road, and autonomous vehicles must adhere to traffic laws as well.
However, current autonomous vehicles prioritize safety and collision avoidance
primarily in their decision-making and planning, which will lead to
misunderstandings and distrust from human drivers and may even result in
accidents in mixed traffic flow. Therefore, ensuring the compliance of the
autonomous driving decision-making system is essential for ensuring the safety
of autonomous driving and promoting the widespread adoption of autonomous
driving technology. To this end, the paper proposes a trigger-based layered
compliance decision-making framework. This framework utilizes the decision
intent at the highest level as a signal to activate an online violation monitor
that identifies the type of violation committed by the vehicle. Then, a
four-layer architecture for compliance decision-making is employed to generate
compliantly trajectories. Using this system, autonomous vehicles can detect and
correct potential violations in real-time, thereby enhancing safety and
building public confidence in autonomous driving technology. Finally, the
proposed method is evaluated on the DJI AD4CHE highway dataset under four
typical highway scenarios: speed limit, following distance, overtaking, and
lane-changing. The results indicate that the proposed method increases the
vehicle's overall compliance rate from 13.85% to 84.46%, while reducing the
proportion of active violations to 0%, demonstrating its effectiveness.Comment: 14 pages, 17 figure
Learning Based High-Level Decision Making for Abortable Overtaking in Autonomous Vehicles
Autonomous vehicles are a growing technology that aims to enhance safety,
accessibility, efficiency, and convenience through autonomous maneuvers ranging
from lane change to overtaking. Overtaking is one of the most challenging
maneuvers for autonomous vehicles, and current techniques for autonomous
overtaking are limited to simple situations. This paper studies how to increase
safety in autonomous overtaking by allowing the maneuver to be aborted. We
propose a decision-making process based on a deep Q-Network to determine if and
when the overtaking maneuver needs to be aborted. The proposed algorithm is
empirically evaluated in simulation with varying traffic situations, indicating
that the proposed method improves safety during overtaking maneuvers.
Furthermore, the approach is demonstrated in real-world experiments using the
autonomous shuttle iseAuto.Comment: 11 pages, 16 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Validation of trajectory planning strategies for automated driving under cooperative, urban, and interurban scenarios.
149 p.En esta Tesis se estudia, diseña e implementa una arquitectura de control para vehículos automatizados de forma dual, que permite realizar pruebas en simulación y en vehículos reales con los mínimos cambios posibles. La arquitectura descansa sobre seis módulos: adquisición de información de sensores, percepción del entorno, comunicaciones e interacción con otros agentes, decisión de maniobras, control y actuación, además de la generación de mapas en el módulo de decisión, que utiliza puntos simples para la descripción de las estructuras de la ruta (rotondas, intersecciones, tramos rectos y cambios de carril)Tecnali
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
AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints
We present AutonoVi:, a novel algorithm for autonomous vehicle navigation
that supports dynamic maneuvers and satisfies traffic constraints and norms.
Our approach is based on optimization-based maneuver planning that supports
dynamic lane-changes, swerving, and braking in all traffic scenarios and guides
the vehicle to its goal position. We take into account various traffic
constraints, including collision avoidance with other vehicles, pedestrians,
and cyclists using control velocity obstacles. We use a data-driven approach to
model the vehicle dynamics for control and collision avoidance. Furthermore,
our trajectory computation algorithm takes into account traffic rules and
behaviors, such as stopping at intersections and stoplights, based on an
arc-spline representation. We have evaluated our algorithm in a simulated
environment and tested its interactive performance in urban and highway driving
scenarios with tens of vehicles, pedestrians, and cyclists. These scenarios
include jaywalking pedestrians, sudden stops from high speeds, safely passing
cyclists, a vehicle suddenly swerving into the roadway, and high-density
traffic where the vehicle must change lanes to progress more effectively.Comment: 9 pages, 6 figure
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