Predictive Trajectory Planning in Situations with Hidden Road Users Using Partially Observable Markov Decision Processes

Schörner, Philip; Töttel, Lars; Doll, Jens; Zöllner, J. Marius

doi:10.1109/ivs.2019.8814022

Cited by 32 publications

(21 citation statements)

References 14 publications

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“…In order to reduce the level of computational complexity, the authors proposed a custom POMDP solver designed specifically for their model. In Reference [14], hidden vehicles were modeled with an associated existence probability, which allowed more flexibility than simply assuming the worst-case scenario.…”

Section: Pomdp-based Approachesmentioning

confidence: 99%

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

et al. 2021

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A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility.

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Section: Pomdp-based Approachesmentioning

confidence: 99%

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

et al. 2021

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“…The problem of occluded areas has been addressed in numerous recent approaches [2,3,4,5,6,7,8,9,10,11,12]. They can be classified in different families, according to the way they represent these regions: * 1 Inria Paris, 2 rue Simone Iff 75012 Paris FRANCE {renaud.poncelet,anne.verroust,fawzi.nashashibi}@inria.fr…”

Section: Related Researchmentioning

confidence: 99%

“…Other approaches model the worst case scenario by using virtual vehicles to represent all potential vehicles in occluded areas [3,5,6,7,11,8]. For a risk evaluation purpose, Damerow et al [3] position one virtual car for each relevant lane at the occluded position closest to the upcoming intersection.…”

Section: Virtual Vehiclesmentioning

confidence: 99%

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Safe Geometric Speed Planning Approach for Autonomous Driving through Occluded Intersections

Poncelet

Verroust-Blondet²,

Nashashibi³

2020

2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)

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Autonomous driving in urban environment needs to anticipate a number of dangerous events, such as the presence of moving vehicles in occluded areas. This paper presents an approach computing a safe motion along a fixed path in an urban environment with dynamic vehicles that may be occluded. The method works on the time-path space and uses a visibility graph to compute the speed profile, considering both safety and comfort. Evaluations performed on CARLA simulator on several typical scenarios show that the approach is able to drive safely in presence of hidden obstacles.

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“…CARLA requires a GPU to run, for the simulations we use a dual Xeon processor server with a Nvidia GPU Geforce GTX 1080Ti (3584 Cuda cores) [29]. For both environment Carracing-v0 and CARLA, we design two intrinsic reward functions that will be used during imitation learning as a performance indicator for the expert demonstrations, and as a reward function in the RL phase [30].…”

Section: Simulation Environment and Data Preparationmentioning

confidence: 99%

Optimal Combination of Imitation and Reinforcement Learning for Self-driving Cars

Youssef¹,

Benbrahim²

2019

RIA

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The two steps in human intelligence development, namely, mimicking and tentative application of expertise, are reflected by imitation learning (IL) and reinforcement learning (RL) in artificial intelligence (AI). However, the RL process does not always improve the skills learned from expert demonstrations and enhance the algorithm performance. To solve the problem, this paper puts forward a novel algorithm called optimal combination of imitation and reinforcement learning (OCIRL). First, the concept of deep q-learning from demonstrations (DQfD) was introduced to the actor-critic (A2C) model, creating the A2CfD model. Then, a threshold was estimated from a trained IL model with the same inputs and reward function with the DOfD, and applied to the A2CfD model. The threshold represents the minimum reward that conserves the learned expertise. The resulting A2CfDoC model was trained and tested on self-driving cars in both discrete and continuous environments. The results show that the model outperformed several existing algorithms in terms of speed and accuracy.

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Predictive Trajectory Planning in Situations with Hidden Road Users Using Partially Observable Markov Decision Processes

Cited by 32 publications

References 14 publications

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles

Safe Geometric Speed Planning Approach for Autonomous Driving through Occluded Intersections

Optimal Combination of Imitation and Reinforcement Learning for Self-driving Cars

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