Intention-Aware Pedestrian Avoidance

Bandyopadhyay, Tirthankar; Jie, Chong Zhuang; Hsu, David; AngJr., Marcelo H.; Rus, Daniela; Frazzoli, Emilio

doi:10.1007/978-3-319-00065-7_64

Cited by 63 publications

(41 citation statements)

References 18 publications

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“…Cell decomposition was also necessary to apply popular models for handling environment uncertainty, such as Partially Observable Markov Decision Processes (POMDP) and Mixed Observability Markov Decision Processes (MOMDP). In [213,214], other moving obstacles' intentions were inferred in real-time while the robot's motion plan was executed concurrently. POMDPs assume uncertainty in both the robot's motion and in observation and account for this uncertainty in solving for optimal policies, where MOMDPs have extended this idea to situations in which some of the state variables are partially observable and others are full observable [215].…”

Section: Combinatorial Planningmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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Autonomous vehicles are expected to play a key role in the future of urban transportation systems, as they offer potential for additional safety, increased productivity, greater accessibility, better road efficiency, and positive impact on the environment. Research in autonomous systems has seen dramatic advances in recent years, due to the increases in available computing power and reduced cost in sensing and computing technologies, resulting in maturing technological readiness level of fully autonomous vehicles. The objective of this paper is to provide a general overview of the recent developments in the realm of autonomous vehicle software systems. Fundamental components of autonomous vehicle software are reviewed, and recent developments in each area are discussed.

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Section: Combinatorial Planningmentioning

confidence: 99%

Perception, Planning, Control, and Coordination for Autonomous Vehicles

et al. 2017

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“…Intention-Aware Motion Planning (IAMP) denotes a motion planning framework where the uncertainty of human intention is taken into account [1]. The goal position and the trajectory of moving pedestrians can be considered as human intention and used so that a moving robot can avoid pedestrians [37].…”

Section: A Intention-aware Motion Planning and Predictionmentioning

confidence: 99%

“…motion model. In some scenarios, it is possible to infer highlevel human intent using additional information, and thereby perform a better prediction of future human motions [1,2]. These techniques are used to predict the pedestrian trajectories based on environmental information in 2D domains.…”

Section: Introductionmentioning

confidence: 99%

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

Park¹,

Park²,

Manocha

2017

Robotics: Science and Systems XIII

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Abstract-We present a motion planning algorithm to compute collision-free and smooth trajectories for high-DOF robots interacting with humans in a shared workspace. Our approach uses offline learning of human actions along with temporal coherence to predict the human actions. Our intention-aware online planning algorithm uses the learned database to compute a reliable trajectory based on the predicted actions. We represent the predicted human motion using a Gaussian distribution and compute tight upper bounds on collision probabilities for safe motion planning. We highlight the performance of our planning algorithm in complex simulated scenarios and real world benchmarks with 7-DOF robot arms operating in a workspace with a human performing complex tasks. We demonstrate the benefits of our intention-aware planner in terms of computing safe trajectories in such uncertain environments.

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“…Some methods used to predict intent are hidden Markov models [7], [8], Markov decision processes [9], and Gaussian processes or mixture models [10], [11]. These methods attempt to model trajectories and classify the dynamic obstacles' motion according to the corresponding intent.…”

Section: Introductionmentioning

confidence: 99%

A ’Cognitive Driving Framework’ for Collision Avoidance in Autonomous Vehicles

Hamlet¹,

Crane²

2015

ijacsa

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Abstract-The Cognitive Driving Framework is a novel method for forecasting the future states of a multi-agent system that takes into consideration both the intentions of the agents as well as their beliefs about the environment. This is particularly useful for autonomous vehicles operating in an urban environment. The algorithm maintains a posterior probability distribution over agent intents and beliefs in order to more accurately forecast their future behavior. This allows an agent navigating the environment to recognize dangerous situations earlier and more accurately than competing algorithms, therefore allowing the agent take actions in order to prevent collisions. This paper presents the Cognitive Driving Framework in detail and describes its application to intersection navigation for autonomous vehicles. The effects of different parameter choices on the performance of the algorithm are analyzed and experiments are conducted demonstrating the ability of the algorithm to predict and prevent automobile collisions caused by human error in multiple intersection navigation scenarios. The results are compared to the performance of prevailing methods; namely reactionary planning and constant velocity forecasting.

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Intention-Aware Pedestrian Avoidance

Cited by 63 publications

References 18 publications

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Perception, Planning, Control, and Coordination for Autonomous Vehicles

Intention-Aware Motion Planning Using Learning Based Human Motion Prediction

A ’Cognitive Driving Framework’ for Collision Avoidance in Autonomous Vehicles

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