4,869 research outputs found
TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China
TiEV is an autonomous driving platform implemented by Tongji University of
China. The vehicle is drive-by-wire and is fully powered by electricity. We
devised the software system of TiEV from scratch, which is capable of driving
the vehicle autonomously in urban paths as well as on fast express roads. We
describe our whole system, especially novel modules of probabilistic perception
fusion, incremental mapping, the 1st and the 2nd planning and the overall
safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future
Challenge of China held at Changshu. We show our experiences on the development
of autonomous vehicles and future trends
Robust, Informative Human-in-the-Loop Predictions via Empirical Reachable Sets
In order to develop provably safe human-in-the-loop systems, accurate and
precise models of human behavior must be developed. In the case of intelligent
vehicles, one can imagine the need for predicting driver behavior to develop
minimally invasive active safety systems or to safely interact with other
vehicles on the road. We present a optimization based method for approximating
the stochastic reachable set for human-in-the-loop systems. This method
identifies the most precise subset of states that a human driven vehicle may
enter, given some dataset of observed trajectories. We phrase this problem as a
mixed integer linear program, which can be solved using branch and bound
methods. The resulting model uncovers the most representative subset that
encapsulates the likely trajectories, up to some probability threshold, by
optimally rejecting outliers in the dataset. This tool provides set predictions
consisting of trajectories observed from the nonlinear dynamics and behaviors
of the human driven car, and can account for modes of behavior, like the driver
state or intent. This allows us to predict driving behavior over long time
horizons with high accuracy. By using this realistic data and flexible
algorithm, a precise and accurate driver model can be developed to capture
likely behaviors. The resulting prediction can be tailored to an individual for
use in semi-autonomous frameworks or generally applied for autonomous planning
in interactive maneuvers
Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach
Considering that human-driven vehicles and autonomous vehicles (AVs) will
coexist on roads in the future for a long time, how to merge AVs into human
drivers traffic ecology and minimize the effect of AVs and their misfit with
human drivers, are issues worthy of consideration. Moreover, different
passengers have different needs for AVs, thus, how to provide personalized
choices for different passengers is another issue for AVs. Therefore, a
human-like decision making framework is designed for AVs in this paper.
Different driving styles and social interaction characteristics are formulated
for AVs regarding driving safety, ride comfort and travel efficiency, which are
considered in the modeling process of decision making. Then, Nash equilibrium
and Stackelberg game theory are applied to the noncooperative decision making.
In addition, potential field method and model predictive control (MPC) are
combined to deal with the motion prediction and planning for AVs, which
provides predicted motion information for the decision-making module. Finally,
two typical testing scenarios of lane change, i.e., merging and overtaking, are
carried out to evaluate the feasibility and effectiveness of the proposed
decision-making framework considering different human-like behaviors. Testing
results indicate that both the two game theoretic approaches can provide
reasonable human-like decision making for AVs. Compared with the Nash
equilibrium approach, under the normal driving style, the cost value of
decision making using the Stackelberg game theoretic approach is reduced by
over 20%.Comment: Accepted by IEEE T-IT
Self-Driving Cars: A Survey
We survey research on self-driving cars published in the literature focusing
on autonomous cars developed since the DARPA challenges, which are equipped
with an autonomy system that can be categorized as SAE level 3 or higher. The
architecture of the autonomy system of self-driving cars is typically organized
into the perception system and the decision-making system. The perception
system is generally divided into many subsystems responsible for tasks such as
self-driving-car localization, static obstacles mapping, moving obstacles
detection and tracking, road mapping, traffic signalization detection and
recognition, among others. The decision-making system is commonly partitioned
as well into many subsystems responsible for tasks such as route planning, path
planning, behavior selection, motion planning, and control. In this survey, we
present the typical architecture of the autonomy system of self-driving cars.
We also review research on relevant methods for perception and decision making.
Furthermore, we present a detailed description of the architecture of the
autonomy system of the self-driving car developed at the Universidade Federal
do Esp\'irito Santo (UFES), named Intelligent Autonomous Robotics Automobile
(IARA). Finally, we list prominent self-driving car research platforms
developed by academia and technology companies, and reported in the media
A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicles
Self-driving vehicles are a maturing technology with the potential to reshape
mobility by enhancing the safety, accessibility, efficiency, and convenience of
automotive transportation. Safety-critical tasks that must be executed by a
self-driving vehicle include planning of motions through a dynamic environment
shared with other vehicles and pedestrians, and their robust executions via
feedback control. The objective of this paper is to survey the current state of
the art on planning and control algorithms with particular regard to the urban
setting. A selection of proposed techniques is reviewed along with a discussion
of their effectiveness. The surveyed approaches differ in the vehicle mobility
model used, in assumptions on the structure of the environment, and in
computational requirements. The side-by-side comparison presented in this
survey helps to gain insight into the strengths and limitations of the reviewed
approaches and assists with system level design choices
Combining Optimal Control and Learning for Visual Navigation in Novel Environments
Model-based control is a popular paradigm for robot navigation because it can
leverage a known dynamics model to efficiently plan robust robot trajectories.
However, it is challenging to use model-based methods in settings where the
environment is a priori unknown and can only be observed partially through
on-board sensors on the robot. In this work, we address this short-coming by
coupling model-based control with learning-based perception. The learning-based
perception module produces a series of waypoints that guide the robot to the
goal via a collision-free path. These waypoints are used by a model-based
planner to generate a smooth and dynamically feasible trajectory that is
executed on the physical system using feedback control. Our experiments in
simulated real-world cluttered environments and on an actual ground vehicle
demonstrate that the proposed approach can reach goal locations more reliably
and efficiently in novel environments as compared to purely geometric
mapping-based or end-to-end learning-based alternatives. Our approach does not
rely on detailed explicit 3D maps of the environment, works well with low frame
rates, and generalizes well from simulation to the real world. Videos
describing our approach and experiments are available on the project website.Comment: Project website: https://vtolani95.github.io/WayPtNav
An Algorithm for Supervised Driving of Cooperative Semi-Autonomous Vehicles (Extended)
Before reaching full autonomy, vehicles will gradually be equipped with more
and more advanced driver assistance systems (ADAS), effectively rendering them
semi-autonomous. However, current ADAS technologies seem unable to handle
complex traffic situations, notably when dealing with vehicles arriving from
the sides, either at intersections or when merging on highways. The high rate
of accidents in these settings prove that they constitute difficult driving
situations. Moreover, intersections and merging lanes are often the source of
important traffic congestion and, sometimes, deadlocks. In this article, we
propose a cooperative framework to safely coordinate semi-autonomous vehicles
in such settings, removing the risk of collision or deadlocks while remaining
compatible with human driving. More specifically, we present a supervised
coordination scheme that overrides control inputs from human drivers when they
would result in an unsafe or blocked situation. To avoid unnecessary
intervention and remain compatible with human driving, overriding only occurs
when collisions or deadlocks are imminent. In this case, safe overriding
controls are chosen while ensuring they deviate minimally from those originally
requested by the drivers. Simulation results based on a realistic physics
simulator show that our approach is scalable to real-world scenarios, and
computations can be performed in real-time on a standard computer for up to a
dozen simultaneous vehicles
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
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
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