Vehicle Motion Control Interface for L4 automated driving

Implement complete solution of Vehicle Motion Control (VMC) interface for L4 automated driving of commercial heavy vehicle combinations


Automated driving has attracted significant attention from both the industry and the public, notably exemplified by the Vera concept (Fig. 1). The main motivations behind the interest for automated driving are that connected automated vehicles have the potential of enhancing traffic safety, optimizing fuel efficiency, and improving overall traffic flow [1].
The Vehicle Motion Control (VMC), illustrated in Fig. 2, serves as operational core of the automated driving system (ADS). It requires a shared reference for determining the desired vehicle motion within the road plane. The VMC not only coordinates this reference motion across various motion devices but also conveys the status and capabilities of viable motion options to the ADS. This integrated VMC is vital to ensure the safe operation of the vehicle in autonomous mode.
In contrast, the ADS is responsible for tasks such as trajectory planning and object avoidance, while the VMC manages vehicle motion, motion coordination, and power management within the constraints of safe vehicle operation. The vehicle and trailer units swept path is a responsibility of the ADS system to guarantee successful combination trajectories through the driving situation.
The standardized interface between the ADS and the VMC is illustrated in Fig. 2. The standardized interface ensures that in a reference coordinate system the motion can be requested by the ADS, and the ADS receives the status and capabilities of the requests from the VMC. This automation interface is for fully automated driving systems that require an Automated Driving System-Dedicated Vehicle (ADS-DV) with redundancy and vehicle safety.
The control interface applies to the vehicle combinations rather than independent units. The ADS sends requests such as acceleration and curvature to the VMC within the capabilities stipulated by the VMC to ensure safe and efficient motion control. Optionally, the ADS may also send requests for power and energy management to optimize range and mission performance.
The VMC interface includes a vehicle model of the vehicle combination with capabilities that is provided to the ADS. The scope of the model is to be able to plan trajectories of the vehicle combination. The model shall be within agreed Operational Design Domain (ODD) and specified safe operating envelop (SOE). The vehicle model includes the VMC logic including capabilities using the SOEs to avoid the vehicle combination instabilities such as rollover. a prediction model for power and energy management is recommended for the VMC interface.


The objectives of the master's thesis are as follows:
Evolve standardized interfaces between the ADS and the VMC to enable seamless communication for automated driving systems.
Implement an interface that allows the ADS to send motion requests, such as acceleration and curvature, to the VMC while following the VMC-defined safety limits.
Develop a vehicle model for the vehicle combination within the VMC interface to help in trajectory planning and motion prediction.
Ensure that the VMC interface includes a parametrized and regularly updated vehicle model that considers current operating conditions for motion prediction, with a focus on safety and stability.
Develop prediction models for power management and energy management, capable of being shared across VMC and ADS.
Assess performance of the control interface using a high-fidelity simulation environment.
Evolution or adaptation of controllers in ADS and VMC to support the new interfaces between them.

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References:


[1] Montanaro U, Dixit S, Fallah S, et al. Towards connected autonomous driving: review of use-cases. Vehicle System Dynamics. 2018.


Suitable background


The thesis work will include control theory, vehicle dynamics and optimization. The work will be carried out at Volvo Group Trucks Technology. The thesis is recommended for two students with control analysis profile with good mathematical skills.

Thesis Level: Master
Language: English
Starting date: January 2024
Number of students: 2
Tutor:
Maliheh Sadeghi Kati- Volvo GTT
Esteban Gelso– Volvo GTT
Leo Laine – Volvo GTT
Johan Lindberg – Volvo GTT
Nikolce Murgovski - Chalmers


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