9,750 research outputs found
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
Multi-Agent Chance-Constrained Stochastic Shortest Path with Application to Risk-Aware Intelligent Intersection
In transportation networks, where traffic lights have traditionally been used
for vehicle coordination, intersections act as natural bottlenecks. A
formidable challenge for existing automated intersections lies in detecting and
reasoning about uncertainty from the operating environment and human-driven
vehicles. In this paper, we propose a risk-aware intelligent intersection
system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We
cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic
Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming
(ILP) formulation that is scalable in the number of agents' interaction points
(e.g., potential collision points at the intersection). In particular, when the
number of agents within an interaction point is small, which is often the case
in intersections, the ILP has a polynomial number of variables and constraints.
To further improve the running time performance, we show that the collision
risk computation can be performed offline. Additionally, a trajectory
optimization workflow is provided to generate risk-aware trajectories for any
given intersection. The proposed framework is implemented in CARLA simulator
and evaluated under a fully autonomous intersection with AVs only as well as in
a hybrid setup with a signalized intersection for HVs and an intelligent scheme
for AVs. As verified via simulations, the featured approach improves
intersection's efficiency by up to while also conforming to the
specified tunable risk threshold
Emerging privacy challenges and approaches in CAV systems
The growth of Internet-connected devices, Internet-enabled services and Internet of Things systems continues at a rapid pace, and their application to transport systems is heralded as game-changing. Numerous developing CAV (Connected and Autonomous Vehicle) functions, such as traffic planning, optimisation, management, safety-critical and cooperative autonomous driving applications, rely on data from various sources. The efficacy of these functions is highly dependent on the dimensionality, amount and accuracy of the data being shared. It holds, in general, that the greater the amount of data available, the greater the efficacy of the function. However, much of this data is privacy-sensitive, including personal, commercial and research data. Location data and its correlation with identity and temporal data can help infer other personal information, such as home/work locations, age, job, behavioural features, habits, social relationships. This work categorises the emerging privacy challenges and solutions for CAV systems and identifies the knowledge gap for future research, which will minimise and mitigate privacy concerns without hampering the efficacy of the functions
A Risk-Averse Preview-based -Learning Algorithm: Application to Highway Driving of Autonomous Vehicles
A risk-averse preview-based -learning planner is presented for navigation
of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is
represented by a finite-state non-stationary Markov decision process (MDP). A
risk assessment unit module is then presented that leverages the preview
information provided by sensors along with a stochastic reachability module to
assign reward values to the MDP states and update them as scenarios develop. A
sampling-based risk-averse preview-based -learning algorithm is finally
developed that generates samples using the preview information and reward
function to learn risk-averse optimal planning strategies without actual
interaction with the environment. The risk factor is imposed on the objective
function to avoid fluctuation of the values, which can jeopardize the
vehicle's safety and/or performance. The overall hybrid automaton model of the
system is leveraged to develop a feasibility check unit module that detects
unfeasible plans and enables the planner system to proactively react to the
changes of the environment. Theoretical results are provided to bound the
number of samples required to guarantee -optimal planning with a high
probability. Finally, to verify the efficiency of the presented algorithm, its
implementation on highway driving of an autonomous vehicle in a varying traffic
density is considered
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