A Novel Lane Merging Framework with Probabilistic Risk based Lane Selection using Time Scaled Collision Cone

Kumar, Amit; Modh, Adarsh; Babu, Mithun; Gopalakrishnan, Bharath; Krishna, K. Madhava

doi:10.1109/ivs.2018.8500652

Cited by 10 publications

(4 citation statements)

References 17 publications

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“…Noh [43] mentioned the division of dangerous scenarios, using collision time (TTC) as the standard, to evaluate the potential threat, at unsignalized T-intersections [44][45][46].…”

Section: Time To Collision (Ttc)mentioning

confidence: 99%

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Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Zheng

Yang

et al. 2022

Sensors

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With the rapid development of autonomous driving technology, both self-driven and human-driven vehicles will share roads in the future and complex information exchange among vehicles will be required. Therefore, autonomous vehicles need to behave as similar to human drivers as possible, to ensure that their behavior can be effectively understood by the drivers of other vehicles and be more in line with the cognition of humans on driving behavior. Therefore, this paper studies the evaluation function of human drivers, using the method of inverse reinforcement learning, aiming for the learned behavior to better imitate the behavior of human drivers. At the same time, this paper proposes a semi-Markov model, to extract the intentions of surrounding related vehicles and divides them into defensive and cooperative, leading the vehicle to adopt a reasonable response to different types of driving scenarios.

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“…Noh [43] mentioned the division of dangerous scenarios, using collision time (TTC) as the standard, to evaluate the potential threat, at unsignalized T-intersections [44][45][46].…”

Section: Time To Collision (Ttc)mentioning

confidence: 99%

“…Different driving strategies are implemented, according to the respective classification into the various dangerous situations. Noh [43] mentioned the division of dangerous scenarios, using collision time (TTC) as the standard, to evaluate the potential threat, at unsignalized T-intersections [44][45][46].…”

Section: Time To Collision (Ttc)mentioning

confidence: 99%

Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Zheng

Yang

et al. 2022

Sensors

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“…Therefore, Ge et al [19] themselves admitted that further research towards a probabilistic extension of the set-based methods, as the one we propose in this work, is necessary. Other works, like [20], [21], have also addressed risk minimized planning, but none of them includes real-world experiments nor features formal guarantees as far-reaching as those of the set-based methods.…”

Section: A Related Workmentioning

confidence: 99%

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

Müller¹,

Strohbeck²,

Herrmann³

et al. 2021

Preprint

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Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address this challenge with a sampling-based optimization approach. For this, we formulate an optimal control problem that optimizes for low risk and high passenger comfort. The risk is calculated on the basis of the perception information and the respective uncertainty using a risk model. The risk model combines set-based methods and probabilistic approaches. Thus, the approach provides safety guarantees in a probabilistic sense, while for a vanishing risk, the formal safety guarantees of the setbased methods are inherited. By exploring all available behavior options, our approach solves decision making and longitudinal trajectory planning in one step. The available behavior options are provided by a formal representation of the situation context, which is also used to reduce calculation efforts. Occlusions are resolved using the external perception of infrastructure-mounted sensors. Yet, instead of merging external and ego perception with track-to-track fusion, the information is used in parallel. The motion planning scheme is validated through real-world experiments.

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“…A lightweight ground optimized method is presented in [19]. But the methods mentioned above derail when autonomous vehicles perform high-speed maneuvers [20]. To address this we provide a novel system that can provide localization and mapping in real-time which is robust enough to handle high-speed maneuvers.…”

Section: Related Workmentioning

confidence: 99%

SROM: Simple Real-time Odometry and Mapping using LiDAR data for Autonomous Vehicles

Rufus¹,

Nair²,

Kumar³

et al. 2020

Preprint

Self Cite

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In this paper, we present SROM, a novel realtime Simultaneous Localization and Mapping (SLAM) system for autonomous vehicles. The keynote of the paper showcases SROM's ability to maintain localization at low sampling rates or at high linear or angular velocities where most popular LiDAR based localization approaches get degraded fast. We also demonstrate SROM to be computationally efficient and capable of handling high-speed maneuvers. It also achieves low drifts without the need for any other sensors like IMU and/or GPS. Our method has a two-layer structure wherein first, an approximate estimate of the rotation angle and translation parameters are calculated using a Phase Only Correlation (POC) method. Next, we use this estimate as an initialization for a point-toplane ICP algorithm to obtain fine matching and registration.Another key feature of the proposed algorithm is the removal of dynamic objects before matching the scans. This improves the performance of our system as the dynamic objects can corrupt the matching scheme and derail localization. Our SLAM system can build reliable maps at the same time generating high-quality odometry. We exhaustively evaluated the proposed method in many challenging highways/country/urban sequences from the KITTI dataset and the results demonstrate better accuracy in comparisons to other state-of-the-art methods with reduced computational expense aiding in real-time realizations. We have also integrated our SROM system with our in-house autonomous vehicle and compared it with the state-of-the-art methods like LOAM and LeGO-LOAM.

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A Novel Lane Merging Framework with Probabilistic Risk based Lane Selection using Time Scaled Collision Cone

Cited by 10 publications

References 17 publications

Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Human-like Decision Making for Autonomous Vehicles at the Intersection Using Inverse Reinforcement Learning

Motion Planning for Connected Automated Vehicles at Occluded Intersections With Infrastructure Sensors

SROM: Simple Real-time Odometry and Mapping using LiDAR data for Autonomous Vehicles

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