Feedback Enhanced Motion Planning for Autonomous Vehicles

Sun, Ke; Schlotfeldt, Brent; Chaves, Stephen; Martin, Paul; Mandhyan, Gulshan; Kumar, Vijay

doi:10.1109/iros45743.2020.9340951

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…The trajectory can be computed using a graph search in a spatiotemporal lattice [17], in a spatial lattice [18], or by an RRT* (Rapid Random Tree) search [19].…”

Section: B Related Workmentioning

confidence: 99%

Competitive Driving of Autonomous Vehicles

Hartmann,

Shiller,

Azaria

2021

Preprint

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This paper describes Ariel Team's autonomous racing controller for the Indy Autonomous Challenge (IAC) simulation race [1]. IAC is the first multi-vehicle autonomous head-to-head competition, reaching speeds of 300 km/h along an oval track, modeled after the Indianapolis Motor Speedway (IMS).Our racing controller attempts to maximize progress along the track while avoiding collisions with opponent vehicles and obeying the race rules. To this end, the racing controller first computes a race line offline. Then, it repeatedly computes online a small set of dynamically feasible maneuver candidates, each tested for collision with the opponent vehicles. Finally, it selects the maneuver that maximizes progress along the track, taking into account the race line.The maneuver candidates, as well as the predicted trajectories of the opponent vehicles, are approximated using a point mass model. Despite the simplicity of this racing controller, it managed to drive competitively and with no collision with any of the opponent vehicles in the IAC final simulation race.

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“…The trajectory can be computed using a graph search in a spatiotemporal lattice [17], in a spatial lattice [18], or by an RRT* (Rapid Random Tree) search [19].…”

Section: B Related Workmentioning

confidence: 99%

Competitive Driving of Autonomous Vehicles

Hartmann,

Shiller,

Azaria

2021

Preprint

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“…Simple parametric models, such as IDM [1] and MO-BIL [2], may serve this purpose. Works like [3], [4] use such models for traffic dynamics and plan motions for the ego vehicle therein. However, these models are designed for studying the macro traffic behavior.…”

Section: Introductionmentioning

confidence: 99%

RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics

Sun¹,

Chaves²,

Martin³

et al. 2022

Preprint

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Modeling stochastic traffic dynamics is critical to developing self-driving cars. Because it is difficult to develop first principle models of cars driven by humans, there is great potential for using data driven approaches in developing traffic dynamical models. While there is extensive literature on this subject, previous works mainly address the prediction accuracy of data-driven models. Moreover, it is often difficult to apply these models to common planning frameworks since they fail to meet the assumptions therein. In this work, we propose a new stochastic traffic model, Recurrent Traffic Graph Neural Network (RTGNN), by enforcing additional structures on the model so that the proposed model can be seamlessly integrated with existing motion planning algorithms. RTGNN is a Markovian model and is able to infer future traffic states conditioned on the motion of the ego vehicle. Specifically, RTGNN uses a definition of the traffic state that includes the state of all players in a local region and is therefore able to make joint predictions for all agents of interest. Meanwhile, we explicitly model the hidden states of agents, "intentions," as part of the traffic state to reflect the inherent partial observability of traffic dynamics. The above mentioned properties are critical for integrating RTGNN with motion planning algorithms coupling prediction and decision making. Despite the additional structures, we show that RTGNN is able to achieve state-of-the-art accuracy through comparisons with other similar works.

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“…However, the high dimension of the state space makes the lattice expensive to be repeatedly constructed and searched in a dynamical environment. Recently, Sun and et al attempted to address this problem by using intelligent driver model (IDM) as a velocity feedback policy [12]. Therefore, the number of dimensions of the lattice planner is reduced.…”

Section: Introductionmentioning

confidence: 99%

Development and Verification of Infrastructure-Assisted Automated Driving Functions

et al. 2021

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Automated vehicles we have on public roads today are capable of up to SAE Level-3 conditional autonomy according to the SAE J3016 Standard taxonomy, where the driver is the main responsible for the driving safety. All the decision-making processes of the system depend on computations performed on the ego vehicle and utilizing only on-board sensor information, mimicking the perception of a human driver. It can be conjectured that for higher levels of autonomy, on-board sensor information will not be sufficient alone. Infrastructure assistance will, therefore, be necessary to ensure the partial or full responsibility of the driving safety. With higher penetration rates of automated vehicles however, new problems will arise. It is expected that automated driving and particularly automated vehicle platoons will lead to more road damage in the form of rutting. Inspired by this, the EU project ESRIUM investigates infrastructure assisted routing recommendations utilizing C-ITS communications. In this respect, specially designed ADAS functions are being developed with capabilities to adapt their behavior according to specific routing recommendations. Automated vehicles equipped with such ADAS functions will be able to reduce road damage. The current paper presents the specific use cases, as well as the developed C-ITS assisted ADAS functions together with their verification results utilizing a simulation framework.

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Feedback Enhanced Motion Planning for Autonomous Vehicles

Cited by 10 publications

References 20 publications

Competitive Driving of Autonomous Vehicles

Competitive Driving of Autonomous Vehicles

RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics

Development and Verification of Infrastructure-Assisted Automated Driving Functions

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