Building a Winning Self-Driving Car in Six Months

Burnett, Keenan; Schimpe, Andreas; Samavi, Sepehr; Gridseth, Mona; Liu, Chengzhi Winston; Li, Qiyang; Kroeze, Zachary; Schoellig, Angela P.

doi:10.1109/icra.2019.8794029

Cited by 14 publications

(7 citation statements)

References 25 publications

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“…For simulations, the reference block generates reference velocity, whose values are predefined as a function of time. In addition to this, the reference block simulates the human operator using a Feedback Linearized Path Tracking Controller (FBLC), taken from [20]. The control law used to simulate the human operator is defined as follows…”

Section: Simulation Resultsmentioning

confidence: 99%

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Saparia¹,

Schimpe²,

Ferranti³

2021

Preprint

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Autonomous cars can reduce road traffic accidents and provide a safer mode of transport. However, key technical challenges, such as safe navigation in complex urban environments, need to be addressed before deploying these vehicles on the market. Teleoperation can help smooth the transition from human operated to fully autonomous vehicles since it still has human in the loop providing the scope of fallback on driver. This paper presents an Active Safety System (ASS) approach for teleoperated driving. The proposed approach helps the operator ensure the safety of the vehicle in complex environments, that is, avoid collisions with static or dynamic obstacles. Our ASS relies on a model predictive control (MPC) formulation to control both the lateral and longitudinal dynamics of the vehicle. By exploiting the ability of the MPC framework to deal with constraints, our ASS restricts the controller's authority to intervene for lateral correction of the human operator's commands, avoiding counter-intuitive driving experience for the human operator. Further, we design a visual feedback to enhance the operator's trust over the ASS. In addition, we propose an MPC's prediction horizon data based novel predictive display to mitigate the effects of large latency in the teleoperation system. We tested the performance of the proposed approach on a high-fidelity vehicle simulator in the presence of dynamic obstacles and latency.

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Section: Simulation Resultsmentioning

confidence: 99%

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Saparia¹,

Schimpe²,

Ferranti³

2021

Preprint

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“…The desired path is only known to the teleoperator who is generating the steering input reference. The teleoperator is simulated using a feedback-linearized path-tracking controller, taken from [20]. Omitting the time index, the control law is…”

Section: Simulation Resultsmentioning

confidence: 99%

Steer with Me: A Predictive, Potential Field-Based Control Approach for Semi-Autonomous, Teleoperated Road Vehicles

Schimpe¹,

Diermeyer²

2020

Preprint

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Autonomous driving is among the most promising of upcoming traffic safety technologies. Prototypes of autonomous vehicles are already being tested on public streets today. However, while current prototypes prove the feasibility of truly driverless cars, edge cases remain which necessitate falling back on human operators. Teleoperated driving is one solution that would allow a human to remotely control a vehicle via mobile radio networks. Removing in-vehicle drivers would thus allow current autonomous technologies to further progress towards becoming genuinely driverless systems. This paper proposes a new model predictive steering control scheme, specifically designed for semi-autonomous, teleoperated road vehicles. The controller is capable of receiving teleoperator steering commands and, in the case of potential collisions, automatically correcting these commands. Collision avoidance is incorporated into the design using potential fields. A term in the cost function facilitates natural maneuvers, and constraints on the maximum potential keep the vehicle at safe distances from obstacles. This paper also proposes the use of high-order ellipses as a method to accurately model rectangular obstacles in tight driving scenarios. Simulation results support the effectiveness of the proposed approach.

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“…In the presented simulations, the steering actions from the human operator are simulated through a feedback linearization-based path tracking controller [25], following a straight reference path with a constant desired velocity of 5 m/s. The scenario, visualized in the xy plane in Fig.…”

Section: A Simulation Setupmentioning

confidence: 99%

Steering Action-aware Adaptive Cruise Control for Teleoperated Driving

Schimpe¹,

Majstorovic²,

Diermeyer³

2022

Preprint

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In this paper, a steering action-aware Adaptive Cruise Control (ACC) approach for teleoperated road vehicles is proposed. In order to keep the vehicle in a safe state, the ACC approach can override the human operator's velocity control commands. The safe state is defined as a state from which the vehicle can be stopped safely, no matter which steering actions are applied by the operator. This is achieved by first sampling various potential future trajectories. In a second stage, assuming the trajectory with the highest risk, a safe and comfortable velocity profile is optimized. This yields a safe velocity control command for the vehicle. In simulations, the characteristics of the approach are compared to a Model Predictive Control-based approach that is capable of overriding both, the commanded steering angle as well as the velocity. Furthermore, in teleoperation experiments with a 1:10-scale vehicle testbed, it is demonstrated that the proposed ACC approach keeps the vehicle safe, even if the control commands from the operator would have resulted in a collision.

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Building a Winning Self-Driving Car in Six Months

Cited by 14 publications

References 25 publications

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Active Safety System for Semi-Autonomous Teleoperated Vehicles

Steer with Me: A Predictive, Potential Field-Based Control Approach for Semi-Autonomous, Teleoperated Road Vehicles

Steering Action-aware Adaptive Cruise Control for Teleoperated Driving

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