98 research outputs found
Attention and Sensor Planning in Autonomous Robotic Visual Search
This thesis is concerned with the incorporation of saliency in visual search and the development of sensor planning strategies for visual search. The saliency model is a mixture of two schemes that extracts visual clues regarding the structure of the environment and object specific features. The sensor planning methods, namely Greedy Search with Constraint (GSC), Extended Greedy Search (EGS) and Dynamic Look Ahead Search (DLAS) are approximations to the optimal solution for the problem of object search, as extensions to the work of Yiming Ye.
Experiments were conducted to evaluate the proposed methods. They show that by using saliency in search a performance improvement up to 75% is attainable in terms of number of actions taken to complete the search. As for the planning strategies, the GSC algorithm achieved the highest detection rate and the best efficiency in terms of cost it incurs to explore every percentage of an environment
Agreeing to Cross: How Drivers and Pedestrians Communicate
The contribution of this paper is twofold. The first is a novel dataset for
studying behaviors of traffic participants while crossing. Our dataset contains
more than 650 samples of pedestrian behaviors in various street configurations
and weather conditions. These examples were selected from approx. 240 hours of
driving in the city, suburban and urban roads. The second contribution is an
analysis of our data from the point of view of joint attention. We identify
what types of non-verbal communication cues road users use at the point of
crossing, their responses, and under what circumstances the crossing event
takes place. It was found that in more than 90% of the cases pedestrians gaze
at the approaching cars prior to crossing in non-signalized crosswalks. The
crossing action, however, depends on additional factors such as time to
collision (TTC), explicit driver's reaction or structure of the crosswalk.Comment: 6 pages, 6 figure
IMPOSITION: Implicit Backdoor Attack through Scenario Injection
This paper presents a novel backdoor attack called IMPlicit BackdOor Attack
through Scenario InjecTION (IMPOSITION) that does not require direct poisoning
of the training data. Instead, the attack leverages a realistic scenario from
the training data as a trigger to manipulate the model's output during
inference. This type of attack is particularly dangerous as it is stealthy and
difficult to detect. The paper focuses on the application of this attack in the
context of Autonomous Driving (AD) systems, specifically targeting the
trajectory prediction module. To implement the attack, we design a trigger
mechanism that mimics a set of cloned behaviors in the driving scene, resulting
in a scenario that triggers the attack. The experimental results demonstrate
that IMPOSITION is effective in attacking trajectory prediction models while
maintaining high performance in untargeted scenarios. Our proposed method
highlights the growing importance of research on the trustworthiness of Deep
Neural Network (DNN) models, particularly in safety-critical applications.
Backdoor attacks pose a significant threat to the safety and reliability of DNN
models, and this paper presents a new perspective on backdooring DNNs. The
proposed IMPOSITION paradigm and the demonstration of its severity in the
context of AD systems are significant contributions of this paper. We highlight
the impact of the proposed attacks via empirical studies showing how IMPOSITION
can easily compromise the safety of AD systems
CRITERIA: a New Benchmarking Paradigm for Evaluating Trajectory Prediction Models for Autonomous Driving
Benchmarking is a common method for evaluating trajectory prediction models
for autonomous driving. Existing benchmarks rely on datasets, which are biased
towards more common scenarios, such as cruising, and distance-based metrics
that are computed by averaging over all scenarios. Following such a regiment
provides a little insight into the properties of the models both in terms of
how well they can handle different scenarios and how admissible and diverse
their outputs are. There exist a number of complementary metrics designed to
measure the admissibility and diversity of trajectories, however, they suffer
from biases, such as length of trajectories.
In this paper, we propose a new benChmarking paRadIgm for evaluaTing
trajEctoRy predIction Approaches (CRITERIA). Particularly, we propose 1) a
method for extracting driving scenarios at varying levels of specificity
according to the structure of the roads, models' performance, and data
properties for fine-grained ranking of prediction models; 2) A set of new
bias-free metrics for measuring diversity, by incorporating the characteristics
of a given scenario, and admissibility, by considering the structure of roads
and kinematic compliancy, motivated by real-world driving constraints. 3) Using
the proposed benchmark, we conduct extensive experimentation on a
representative set of the prediction models using the large scale Argoverse
dataset. We show that the proposed benchmark can produce a more accurate
ranking of the models and serve as a means of characterizing their behavior. We
further present ablation studies to highlight contributions of different
elements that are used to compute the proposed metrics
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