66,829 research outputs found
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
Towards Social Autonomous Vehicles: Efficient Collision Avoidance Scheme Using Richardson's Arms Race Model
Background Road collisions and casualties pose a serious threat to commuters
around the globe. Autonomous Vehicles (AVs) aim to make the use of technology
to reduce the road accidents. However, the most of research work in the context
of collision avoidance has been performed to address, separately, the rear end,
front end and lateral collisions in less congested and with high
inter-vehicular distances. Purpose The goal of this paper is to introduce the
concept of a social agent, which interact with other AVs in social manners like
humans are social having the capability of predicting intentions, i.e.
mentalizing and copying the actions of each other, i.e. mirroring. The proposed
social agent is based on a human-brain inspired mentalizing and mirroring
capabilities and has been modelled for collision detection and avoidance under
congested urban road traffic.
Method We designed our social agent having the capabilities of mentalizing
and mirroring and for this purpose we utilized Exploratory Agent Based Modeling
(EABM) level of Cognitive Agent Based Computing (CABC) framework proposed by
Niazi and Hussain.
Results Our simulation and practical experiments reveal that by embedding
Richardson's arms race model within AVs, collisions can be avoided while
travelling on congested urban roads in a flock like topologies. The performance
of the proposed social agent has been compared at two different levels.Comment: 48 pages, 21 figure
Limited Visibility and Uncertainty Aware Motion Planning for Automated Driving
Adverse weather conditions and occlusions in urban environments result in
impaired perception. The uncertainties are handled in different modules of an
automated vehicle, ranging from sensor level over situation prediction until
motion planning. This paper focuses on motion planning given an uncertain
environment model with occlusions. We present a method to remain collision free
for the worst-case evolution of the given scene. We define criteria that
measure the available margins to a collision while considering visibility and
interactions, and consequently integrate conditions that apply these criteria
into an optimization-based motion planner. We show the generality of our method
by validating it in several distinct urban scenarios
An Agent-based Modelling Framework for Driving Policy Learning in Connected and Autonomous Vehicles
Due to the complexity of the natural world, a programmer cannot foresee all
possible situations, a connected and autonomous vehicle (CAV) will face during
its operation, and hence, CAVs will need to learn to make decisions
autonomously. Due to the sensing of its surroundings and information exchanged
with other vehicles and road infrastructure, a CAV will have access to large
amounts of useful data. While different control algorithms have been proposed
for CAVs, the benefits brought about by connectedness of autonomous vehicles to
other vehicles and to the infrastructure, and its implications on policy
learning has not been investigated in literature. This paper investigates a
data driven driving policy learning framework through an agent-based modelling
approaches. The contributions of the paper are two-fold. A dynamic programming
framework is proposed for in-vehicle policy learning with and without
connectivity to neighboring vehicles. The simulation results indicate that
while a CAV can learn to make autonomous decisions, vehicle-to-vehicle (V2V)
communication of information improves this capability. Furthermore, to overcome
the limitations of sensing in a CAV, the paper proposes a novel concept for
infrastructure-led policy learning and communication with autonomous vehicles.
In infrastructure-led policy learning, road-side infrastructure senses and
captures successful vehicle maneuvers and learns an optimal policy from those
temporal sequences, and when a vehicle approaches the road-side unit, the
policy is communicated to the CAV. Deep-imitation learning methodology is
proposed to develop such an infrastructure-led policy learning framework
- …