996 research outputs found
Multi-Lane Perception Using Feature Fusion Based on GraphSLAM
An extensive, precise and robust recognition and modeling of the environment
is a key factor for next generations of Advanced Driver Assistance Systems and
development of autonomous vehicles. In this paper, a real-time approach for the
perception of multiple lanes on highways is proposed. Lane markings detected by
camera systems and observations of other traffic participants provide the input
data for the algorithm. The information is accumulated and fused using
GraphSLAM and the result constitutes the basis for a multilane clothoid model.
To allow incorporation of additional information sources, input data is
processed in a generic format. Evaluation of the method is performed by
comparing real data, collected with an experimental vehicle on highways, to a
ground truth map. The results show that ego and adjacent lanes are robustly
detected with high quality up to a distance of 120 m. In comparison to serial
lane detection, an increase in the detection range of the ego lane and a
continuous perception of neighboring lanes is achieved. The method can
potentially be utilized for the longitudinal and lateral control of
self-driving vehicles
Multi-Object Tracking with Interacting Vehicles and Road Map Information
In many applications, tracking of multiple objects is crucial for a
perception of the current environment. Most of the present multi-object
tracking algorithms assume that objects move independently regarding other
dynamic objects as well as the static environment. Since in many traffic
situations objects interact with each other and in addition there are
restrictions due to drivable areas, the assumption of an independent object
motion is not fulfilled. This paper proposes an approach adapting a
multi-object tracking system to model interaction between vehicles, and the
current road geometry. Therefore, the prediction step of a Labeled
Multi-Bernoulli filter is extended to facilitate modeling interaction between
objects using the Intelligent Driver Model. Furthermore, to consider road map
information, an approximation of a highly precise road map is used. The results
show that in scenarios where the assumption of a standard motion model is
violated, the tracking system adapted with the proposed method achieves higher
accuracy and robustness in its track estimations
Overview of Environment Perception for Intelligent Vehicles
This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The
state-of-the-art algorithms and modeling methods for intelligent
vehicles are given, with a summary of their pros and cons. A
special attention is paid to methods for lane and road detection,
traffic sign recognition, vehicle tracking, behavior analysis, and
scene understanding. In addition, we provide information about
datasets, common performance analysis, and perspectives on
future research directions in this area
Developing Predictive Models of Driver Behaviour for the Design of Advanced Driving Assistance Systems
World-wide injuries in vehicle accidents have been on the rise in recent
years, mainly due to driver error. The main objective of this research is to
develop a predictive system for driving maneuvers by analyzing the cognitive
behavior (cephalo-ocular) and the driving behavior of the driver (how the vehicle
is being driven). Advanced Driving Assistance Systems (ADAS) include
different driving functions, such as vehicle parking, lane departure warning,
blind spot detection, and so on. While much research has been performed on
developing automated co-driver systems, little attention has been paid to the
fact that the driver plays an important role in driving events. Therefore, it
is crucial to monitor events and factors that directly concern the driver. As
a goal, we perform a quantitative and qualitative analysis of driver behavior
to find its relationship with driver intentionality and driving-related actions.
We have designed and developed an instrumented vehicle (RoadLAB) that is
able to record several synchronized streams of data, including the surrounding
environment of the driver, vehicle functions and driver cephalo-ocular behavior,
such as gaze/head information. We subsequently analyze and study the
behavior of several drivers to find out if there is a meaningful relation between
driver behavior and the next driving maneuver
Learning Multi-Modal Self-Awareness Models Empowered by Active Inference for Autonomous Vehicles
For autonomous agents to coexist with the real world, it is essential to anticipate the dynamics and interactions in their surroundings. Autonomous agents can use models of the human
brain to learn about responding to the actions of other participants in the environment and proactively coordinates with the dynamics. Modeling brain learning procedures is challenging for multiple reasons, such as stochasticity, multi-modality, and unobservant intents. A neglected problem has long been understanding and processing environmental
perception data from the multisensorial information referring to the cognitive psychology level of the human brain process. The key to solving this problem is to construct a computing model with selective attention and self-learning ability for autonomous driving, which is
supposed to possess the mechanism of memorizing, inferring, and experiential updating, enabling it to cope with the changes in an external world. Therefore, a practical self-driving approach should be open to more than just the traditional computing structure of perception, planning, decision-making, and control. It is necessary to explore a probabilistic
framework that goes along with human brain attention, reasoning, learning, and decisionmaking mechanism concerning interactive behavior and build an intelligent system inspired by biological intelligence.
This thesis presents a multi-modal self-awareness module for autonomous driving systems. The techniques proposed in this research are evaluated on their ability to model proper driving behavior in dynamic environments, which is vital in autonomous driving for both action
planning and safe navigation. First, this thesis adapts generative incremental learning to the problem of imitation learning. It extends the imitation learning framework to work in the multi-agent setting where observations gathered from multiple agents are used to
inform the training process of a learning agent, which tracks a dynamic target. Since driving has associated rules, the second part of this thesis introduces a method to provide optimal knowledge to the imitation learning agent through an active inference approach.
Active inference is the selective information method gathering during prediction to increase a predictive machine learning model’s prediction performance. Finally, to address the inference complexity and solve the exploration-exploitation dilemma in unobserved environments, an exploring action-oriented model is introduced by pulling together imitation learning and active inference methods inspired by the brain learning procedure
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