KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long- Term Trajectory Prediction

Lu, Qiujing; Han, Weiqiao; Ling, Jeffrey; Wang, Minfa; Chen, Haoyu; Varadarajan, Balakrishnan; Covington, Paul S.

doi:10.1109/icra46639.2022.9812337

Cited by 12 publications

(4 citation statements)

References 19 publications

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“…This is a reasonable assumption, because there are many techniques that predict the future trajectories of obstacles. For example, in the recently developed autonomous driving research community, there is a whole area devoted to predicting the future trajectories of agents, including vehicles, bicyclists, and pedestrians, in the traffic scene Gu et al (2021); Lu et al (2022). After getting information from the environment via perception, the robot first predicts the future trajectories of other agents in the scene and then plans its own trajectory.…”

Section: Problem Formulationmentioning

confidence: 99%

Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

Jasour¹,

Han²,

Williams³

2021

Robotics: Science and Systems XVII

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In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk bounded trajectories without time discretization. The provided approach deals with arbitrary probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems.

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Section: Problem Formulationmentioning

confidence: 99%

Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

Jasour¹,

Han²,

Williams³

2021

Robotics: Science and Systems XVII

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show abstract

Section: Problem Formulationmentioning

confidence: 99%

“…In essence, we are considering the trajectory planning problem in the presence of “known unknowns.” This is a reasonable assumption, because there are many techniques that predict the future trajectories of obstacles. For example, in the recently developed autonomous driving research community, there is a whole area devoted to predicting the future trajectories of agents, including vehicles, bicyclists, and pedestrians, in the traffic scene (Gu et al, 2021; Lu et al, 2022). After getting information from the environment via perception, the robot first predicts the future trajectories of other agents in the scene and then plans its own trajectory.…”

Section: Problem Formulationmentioning

confidence: 99%

Convex risk-bounded continuous-time trajectory planning and tube design in uncertain nonconvex environments

Jasour,

Han,

Williams

2023

The International Journal of Robotics Research

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In this paper, we address the trajectory planning problem in uncertain nonconvex static and dynamic environments that contain obstacles with probabilistic location, size, and geometry. To address this problem, we provide a risk-bounded trajectory planning method that looks for continuous-time trajectories with guaranteed bounded risk over the planning time horizon. Risk is defined as the probability of collision with uncertain obstacles. Existing approaches to address risk-bounded trajectory planning problems either are limited to Gaussian uncertainties and convex obstacles or rely on sampling-based methods that need uncertainty samples and time discretization. To address the risk-bounded trajectory planning problem, we leverage the notion of risk contours to transform the risk-bounded planning problem into a deterministic optimization problem. Risk contours are the set of all points in the uncertain environment with guaranteed bounded risk. The obtained deterministic optimization is, in general, nonlinear and nonconvex time-varying optimization. We provide convex methods based on sum-of-squares optimization to efficiently solve the obtained nonconvex time-varying optimization problem and obtain the continuous-time risk-bounded trajectories without time discretization. The provided approach deals with arbitrary (and known) probabilistic uncertainties, nonconvex and nonlinear, static and dynamic obstacles, and is suitable for online trajectory planning problems. In addition, we provide convex methods based on sum-of-squares optimization to build the max-sized tube with respect to its parameterization along the trajectory so that any state inside the tube is guaranteed to have bounded risk.

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“…If the environment is static, then risk contours can be generated offline. If the environment is highly dynamic, such as a traffic scene, and if the robot is an autonomous driving car with strong computing power, then the prediction module of the car predicts the future trajectories of surrounding vehicles at high frequency [26,27] and risk contours can be form at high frequency, too.…”

Section: F Theoretical Guarantees and Summarymentioning

confidence: 99%

Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments

Han¹,

Jasour²,

Williams³

2022

2022 International Conference on Robotics and Automation (ICRA)

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We consider the motion planning problem for stochastic nonlinear systems in uncertain environments. More precisely, in this problem the robot has stochastic nonlinear dynamics and uncertain initial locations, and the environment contains multiple dynamic uncertain obstacles. Obstacles can be of arbitrary shape, can deform, and can move. All uncertainties do not necessarily have Gaussian distribution. This general setting has been considered and solved in [1]. In addition to the assumptions above, in this paper, we consider long-term tasks, where the planning method in [1] would fail, as the uncertainty of the system states grows too large over a long time horizon. Unlike [1], we present a real-time online motion planning algorithm. We build discrete-time motion primitives and their corresponding continuous-time tubes offline, so that almost all system states of each motion primitive are guaranteed to stay inside the corresponding tube. We convert probabilistic safety constraints into a set of deterministic constraints called risk contours. During online execution, we verify the safety of the tubes against deterministic risk contours using sum-ofsquares (SOS) programming. The provided SOS-based method verifies the safety of the tube in the presence of uncertain obstacles without the need for uncertainty samples and time discretization in real-time. By bounding the probability the system states staying inside the tube and bounding the probability of the tube colliding with obstacles, our approach guarantees bounded probability of system states colliding with obstacles. We demonstrate our approach on several long-term robotics tasks.

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KEMP: Keyframe-Based Hierarchical End-to-End Deep Model for Long- Term Trajectory Prediction

Cited by 12 publications

References 19 publications

Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

Convex Risk Bounded Continuous-Time Trajectory Planning in Uncertain Nonconvex Environments

Convex risk-bounded continuous-time trajectory planning and tube design in uncertain nonconvex environments

Non-Gaussian Risk Bounded Trajectory Optimization for Stochastic Nonlinear Systems in Uncertain Environments

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