Enhanced intelligent driver model for two-dimensional motion planning in mixed traffic

Sharath, M.N.; Velaga, Nagendra R.

doi:10.1016/j.trc.2020.102780

Cited by 55 publications

(15 citation statements)

References 41 publications

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“…On similar lines, few other studies reported substantial lateral behavior of vehicles and smaller vehicles' dominance in the traffic stream (Bharadwaj et al 2016;Raju et al 2018). In this domain (Sharath and Velaga 2020;Raju, Arkatkar, and Gaurang 2019), highlighted the importance of surrounding vehicles' can impact the vehicle's movement in a mixed traffic stream. Further, a study by Patil et al (2021) explicitly highlights the importance of surrounding vehicles in surrogate traffic safety.…”

Section: Introductionsupporting

confidence: 52%

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Raju

Arkatkar

Easa

et al. 2021

Transportmetrica B: Transport Dynamics

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This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles' movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models.

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Section: Introductionsupporting

confidence: 52%

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Raju

Arkatkar

Easa

et al. 2021

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

show abstract

“…Thus, deploying a microscopic traffic simulation directly in an autonomous-driving test may lead to biased evaluation results [1]. Some studies have attempted to improve the microscopic traffic-flow model, such as that by Sharath and Velaga, which developed a two-dimensional motion model using the intelligent driver model (IDM) [5]; a study by Mullakkal-Babu et al described the lateral motion and interaction between vehicles by introducing a bicycledynamics model [6]. However, these changes are still centered on the IDM car-following model, which is not fundamentally different from the traditional microscopic traffic-flow approach.…”

Section: Related Workmentioning

confidence: 99%

Motion Planning for Dynamic Scenario Vehicles in Autonomous-Driving Simulations

2023

IEEE Access

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Scenario vehicles are an important part of the dynamic environment utilized in autonomousdriving simulations. They are required to meet the demands of traffic-scenario diversity and form a larger coverage scale in the road network. However, the current motion planning of scenario vehicles either adheres to the classical microscopic traffic-flow model or follows a predefined path; thus, interacting with the vehicle under test in a dynamic bidirectional fashion is difficult. This study researches a motion-planning method for a broader category of unmanned vehicles and proposes a motion-planning method for scenario vehicles based on Pontryagin's minimal principle, used in optimal control theory and the closed-form solution of the minimum snap method. The study reclassifies actions received from the behavior layer according to the boundary conditions and final times and derives an analytical solution for each of them. The analytical solution is then experimentally verified. The proposed method not only accomplishes efficient motion planning but also exhibits variant driving styles, which provides a practical solution for the motion planning of scenario vehicles in simulations.

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“…To validate the humanlike ability of the proposed humanlike LC (HLC) algorithm, comparison HLC with the AV-IDM [49] and nonlinear model predictive control (NMPC) [25] are applied to NDD. In addition, they are compared in some complex LC scenarios [50].…”

Section: Algorithm Validation and Analysismentioning

confidence: 99%

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

Gao

2022

IEEE Access

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The autonomous vehicles (AVs) need to share the driving environment with the human driving vehicles (HDVs) on expressway in the future. The non-humanlike lane changing (LC) behavior of AVs can mislead human drivers, which brings potential risks. Stronger humanlike ability requires a more complex algorithm. However, the requirement of the on-board vehicle computation resources limits the humanlike ability of LC algorithms. In this context, considering environmental risks, driver speed requirements and driver focus shifting process, this paper proposes a new type of LC algorithm based on artificial potential field (APF) aiming to improve the humanlike ability. The coupling relationship between the longitudinal and the lateral potential field forces is analyzed theoretically based on environmental risk APF. Based on the relationship, we proposed a conversion mechanism of the driver between the lateral and longitudinal potential field forces to mimic the driver's speed requirement. Then a target lane selection strategy is designed to trade-off the driver's speed requirement and safety between multiple lanes. Finally, to mimic the driver's focus transfer process, the lateral forces of the ego vehicle are analyzed theoretically to further propose a moving virtual lane lines algorithm to build the lateral space varying potential field. The algorithm proposed in this paper is validated by comparing with other LC algorithms in the real traffic scenarios. The results indicate that the proposed algorithm has a better ability to mimic human driver's LC decision-making and provides a smoother and safer humanlike trajectory.INDEX TERMS Autonomous vehicles, artificial potential field, lane changing decision-making, motion planning.

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Enhanced intelligent driver model for two-dimensional motion planning in mixed traffic

Cited by 55 publications

References 41 publications

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Customizing the following behavior models to mimic the weak lane based mixed traffic conditions

Motion Planning for Dynamic Scenario Vehicles in Autonomous-Driving Simulations

Humanlike Decision and Motion Planning for Expressway Lane Changing Based on Artificial Potential Field

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