Driving situation analysis based on
multi-model observer: application
to risk accident of heavy vehicles
M. Bouteldja, V. Cerezo
CETE of Lyon – Research team n°12
Laboratoire Régional des Ponts et Chaussées de Lyon
25, Avenue François Mitterrand, 69500 Bron-France
Introduction
Heavy vehicles’ accidents represent:
•  damages on infrastructure, environment
•  great economical and human consequences.
Drivers actions in risky situations are limited to:
•  braking,
•  pushing the throttle pedal,
•  changing the steering angle.
Development of active safety systems to limit dangerous
situations
Need of complex vehicle dynamics model with a lot of parameters
among which some are difficult to obtain,
 Hard to use in real-time detection.
Solution to this problem?
Objectives
The idea :
  Split the trajectory in several “simply driving situations”,
  Represent the truck behavior with a set of simply models.
Objectives :
  Use a simple representation (6 driving situations),
  Develop an accurate truck dynamics observation,
  Calculate only the data necessary for safety,
  Detect rollover risk in real time.
Driving situations
State 1
Longitudinal
constant speed
State 3
Lateral constant
speed
Four dynamical states considered
State 2
Longitudinal
variable speed
State 4
Coupled variable
speed
The criteria of change between the
states is related to the longitudinal
and lateral acceleration
 Use of an automat for switching
between the states
Simplified truck models (1/2)
Pure longitudinal dynamic model (Model 1)
  longitudinal load transfer,
  wind effect,
  slope of the road.
Pure lateral dynamic model (Model 2)
  replace the group of axles by a single
wheel
 rolling phenomenon (add a sprung mass
attached to the chassis through the
suspension),
  lateral load transfer.
Simplified truck models (2/2)
Coupled dynamic model (Model 3)
  4 degrees of freedom,
  roll and yaw angles, longitudinal and lateral velocities needed,
  road crossfall.
Observer
Number of the state measured by the sensor < number of
the state of the system
Non measured
Variables
Dynamics states
Measured
variable
Software sensor based on
the dynamic system theory
Observers
Estimator
Model
Vehicle Model :

Observer :
Knowledge of
the global state
Correction term
(output injection)
Rollover detection
  Use of the load transfer ratio (R=1 or –1)
  Rollover detection
u
Validation of this approach (1/5)
  Validation of the driving situations model versus Prosper simulator
Model 1
Model 2
u
Model 3
PROSPER
u : input
Simulation results of validation
  Example 1: curve
Validation of this approach (2/5)
  Example 2: case of an exit ramp (5 driving situations considered)
Validation of this approach (3/5)
  comparison of the multi-model approach results with PROSPER
results (reference)
  Input: observation of the speed profile
Validation of this approach (4/5)
The multi-model approach
gives similar results to
PROSPER for variables link to
roll and yaw dynamics
The management system
between the simplified
models is working
Last steep: calculus of R to detect rellover
Validation of this approach (5/5)
No rollover risk detected
Same scenario with initial
speed of 110 km/h
Conclusion
  Original approach with the split in simpliy driving situations,
  Development of a simulator and definition of observer /
estimator (parameters and dynamic state),
  Caculus of the usefull dynamics parameters only,
  Validation of the principle on different scenarii,
  Possibility of real-time use to detect rollover,
  Warning strategy and action elaboration in order to prevent
from the risky situation
 with a simple model, the risk is detected
THANK YOU FOR YOUR ATTENTION… Mohamed.bouteldja@developpement-­‐durable.gouv.fr Veronique.cerezo@developpement-­‐durable.gouv.fr