Predictive Threat Assessment in Vehicle Motion Management

Predictive Threat Assessment in Vehicle Motion Management:
Rollover Risk Assessment

Articulated heavy vehicles offer promising economic and environmental advantages for freight transport, but their safety on highways is a concern like jackknifing and rollover due to poor lateral stability on highways. In this context, motion prediction plays a vital role in establishing predictive safety functions for early risk detection and prevention. However, to ensure the safety function is not overly reactive, an accurate risk assessment method is essential for quantifying the level of the risks. Rollovers, as illustrated in Figure 1, represent one of the typical hazardous events associated with heavy trucks.

Fig. 1. Roll over lateral force limit usage on a tractor semitrailer combination.

In the context of vehicle system control, several key components play crucial roles in ensuring safe and efficient operation. These components can include Vehicle Motion Management (VMM), Traffic Situation Management (TSM), and Motion Support Devices (MSD) as shown in Fig. 2.

Fig. 2. Diagram illustrating the system layout.

Within vehicle control framework, VMM plays a crucial role in handling important tasks such as motion estimation, motion prediction, and effective coordination of Motion Support Devices (MSD). In motion prediction, the future motion of 0.5-3 s horizon is predicted based on current automated or manual driver inputs. Multiple parallel predictions can be conducted to proactively prevent violating the control envelope, thereby avoiding severe situations such as rollovers (as shown in Fig. 1), understeer, oversteer, jack-knife incidents, or trailer swingouts.

If the motion prediction is foreseeing a rollover situation in the coming prediction horizon, the vehicle longitudinal velocity capability and acceleration capability to the Traffic Situation Management (TSM) are limited to prevent the hazardous event.

Threat assessment (TA) is an evaluation of events that can adversely affect the vehicle and road users. Various methods such as Threat Metrics, Probabilistic approaches, Formal methods, and Machine learning are employed for TA [3]. By integrating TA with motion prediction, VMM can proactively assess rollover risk, contributing to the prevention of potential accidents.

This master's thesis is composed of the following objectives:

1. Investigating the Swedish road design specification, and state-of-art results of road modelling methods, and accordingly propose potential representation of road characteristics (e.g., inclination, bank, curvature and associated uncertainties)
2. Deriving a vehicle dynamics model tailored for assessing rollover risk.
3. Developing motion prediction algorithms based on vehicle dynamics and road models.
4. Proposing threat assessment methods to quantify the risks.
5. Validating and improving the Predictive Threat Assessment method using high-fidelity vehicle model and real-world data provided by VOLVO.

As a valued thesis worker in our team, you'll receive dedicated support from experienced supervisors. Your project will contribute to our company's vital goals. Join us to make a real impact and advance both your academic journey and professional growth. 

Suitable background

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

Thesis Level: Master
Language: English
Starting date: Jan 2024
Number of students: 2
Tutor:
Leon Henderson, Chalmers & Volvo GTT
Niklas Fröjd, Volvo GTT
Cecilia Bustrén, Volvo GTT
Lei Ni, Volvo GTT
Paolo Falcone, Chalmers

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