A Risk and Comfort Optimizing Motion Planning Scheme for Merging Scenarios

Müller, Johannes; Buchholz, Michael

doi:10.1109/itsc.2019.8917425

Cited by 13 publications

(21 citation statements)

References 17 publications

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“…Predicting the future motion of other traffic participants, implicitly or explicitly as in this work, is key to behavior planning in automated vehicles [1], [2], [3], [4]. This holds in particular for challenging maneuvers like lane merging on highways or crossing an intersection, where accounting for the behavior of other road users is vital for the safe and successful execution of the planned manoeuvre.…”

Section: Introductionmentioning

confidence: 99%

Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks

Strohbeck

Belagiannis

Müller

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Automated vehicles need to not only perceive their environment, but also predict the possible future behavior of all detected traffic participants in order to safely navigate in complex scenarios and avoid critical situations, ranging from merging on highways to crossing urban intersections. Due to the availability of datasets with large numbers of recorded trajectories of traffic participants, deep learning based approaches can be used to model the behavior of road users. This paper proposes a convolutional network that operates on rasterized actor-centric images which encode the static and dynamic actor-environment. We predict multiple possible future trajectories for each traffic actor, which include position, velocity, acceleration, orientation, yaw rate and position uncertainty estimates. To make better use of the past movement of the actor, we propose to employ temporal convolutional networks (TCNs) and rely on uncertainties estimated from the previous object tracking stage. We evaluate our approach on the public "Argoverse Motion Forecasting" dataset, on which it won the first prize at the Argoverse Motion Forecasting Challenge, as presented on the NeurIPS 2019 workshop on "Machine Learning for Autonomous Driving".

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Section: Introductionmentioning

confidence: 99%

Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks

Strohbeck

Belagiannis

Müller

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…Here, we extend the results of [20] by further experiments and show how the reliability of the reliability estimation can be assessed using SL and the proposed urn model. Then, it is shortly sketched how this reliability estimate can be integrated in the motion planning scheme from [19,22]. The second example, which is given in Subsection 4.2 summarizes the approach from [21].…”

Section: Exemplary Sl Applications For Connected Automated Vehiclesmentioning

confidence: 99%

“…As a result, the reliability estimates can be used for decision making in motion planning. Details on our motion planning scheme can be found in [19,22]. As usual in literature, we formulated the motion planning for merging scenarios as Optimal Control Problem (OCP), in which the passenger's comfort and safety are optimized.…”

Section: Reliability Estimation Of Cooperative Informationmentioning

confidence: 99%

Subjective logic reasoning: an urn model intuition and application to connected automated driving

Müller

Buchholz

2021

At - Automatisierungstechnik

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Subjective Logic (SL) is a powerful extend of classical probability theory that can handle small sample sizes and, with that, the resulting statistical uncertainty. However, SL is a quite abstract theory and has found limited attention in the field of automation so far. In this work, we present a new urn model intuition to SL that connects SL with the Pólya urn scheme. The application of SL-based reliability estimation in automation is demonstrated on two examples from the domain of connected automated driving: first to assess external information for motion planning on-board the vehicle and second to rate connected vehicles as agents within a large-scale multi-agent system.

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“…and search for the safe action with the lowest cost regarding comfort and utility. Here we consider actions as jerk commands applied to the automated vehicle similar to [32], [33] which help to provide comfortable maneuvers by moderating jerk. Therefore, we set possible discrete actions as A = {−1.5 m s 3 , 0.0 m s 3 , 1.5 m s 3 }.…”

Section: Learning Safe and Comfortable Policies For Automated Driving Under Uncertaintymentioning

confidence: 99%

Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

Kamran

Engelgeh²,

Busch³

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Despite recent advances in reinforcement learning (RL), its application in safety critical domains like autonomous vehicles is still challenging. Although punishing RL agents for risky situations can help to learn safe policies, it may also lead to highly conservative behavior. In this paper, we propose a distributional RL framework in order to learn adaptive policies that can tune their level of conservativity at run-time based on the desired comfort and utility. Using a proactive safety verification approach, the proposed framework can guarantee that actions generated from RL are fail-safe according to the worst-case assumptions. Concurrently, the policy is encouraged to minimize safety interference and generate more comfortable behavior. We trained and evaluated the proposed approach and baseline policies using a high level simulator with a variety of randomized scenarios including several corner cases which rarely happen in reality but are very crucial. In light of our experiments, the behavior of policies learned using distributional RL can be adaptive at run-time and robust to the environment uncertainty. Quantitatively, the learned distributional RL agent drives in average 8 seconds faster than the normal DQN policy and requires 83% less safety interference compared to the rule-based policy with slightly increasing the average crossing time. We also study sensitivity of the learned policy in environments with higher perception noise and show that our algorithm learns policies that can still drive reliable when the perception noise is two times higher than the training configuration for automated merging and crossing at occluded intersections.

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A Risk and Comfort Optimizing Motion Planning Scheme for Merging Scenarios

Cited by 13 publications

References 17 publications

Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks

Multiple Trajectory Prediction with Deep Temporal and Spatial Convolutional Neural Networks

Subjective logic reasoning: an urn model intuition and application to connected automated driving

Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning

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