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2012-09-21 1 Decision Making and control for automotive safety Mohammad Ali.

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Presentation on theme: "2012-09-21 1 Decision Making and control for automotive safety Mohammad Ali."— Presentation transcript:

1 Decision Making and control for automotive safety Mohammad Ali

2 Financial supporters

3 Outline Motivation, background and challenges Some Suggested approaches and results Concluding remarks

4 Road Injuries Are among the top three causes of death for people 5-44 years old Cost governments 1-3 % of their GDP Cause US$ 518 billion in global losses Source: WHO, ”Global status report on road safety: time for action”, Geneva, 2009

5 Other Collision avoidance systems Aim at avoiding rear end collisions Brake when it is no longer possible to avoid colliding by braking by steering Don’t interfere unless it’s neccessary Often only mitigate Minimal nuisance while providing benefit when possible

6 Wait until it’s unavoidable?

7 Approaches and results

8 objective Utilize knowledge of road geometry to: Avoid loss of control Keep the vehicle on the road Without disturbing the driver

9 Advanced sensing Threat assessment Decision making Path planning challenges Vehicle control

10 Threat assessment problem Vehicle on the road and in the ”linear region”

11 Threat assessment problem ≤ Slip limit ≤ Half the lane width |Slip angle front | | Slip angle rear | | Deviation centerline vehicle corner 1 | | Deviation centerline vehicle corner 2 | | Deviation centerline vehicle corner 3 | | Deviation centerline vehicle corner 4 |

12 Threat assessment problem NowLater Admissible set Given estimates of vehicle state and surrounding environment, can we find an admissible sequence of control signals s.t. the vehicle state evolves within the prescribed constraints? Intervene Dont intervene

13 Reachability based approach NowLater Admissible set 1 Select terminal target set 2 Compute sequence of safe sets 3 Check whether Safe set

14 results Are we overestimating the driver’s capability? Safe set Admissible set Safe set Admissible set

15 Normal drivingRough driving Steering angle Position error Orientation error We can estimate the two gains and the look ahead time! Driver model

16 Results Safe set (driver model) Safe set (no driver model) Admissible set By accounting for driver limitations we can intervene earlier

17 Braking interventions on Braking interventions off Can we make a difference? results

18 results On Off

19 results On Off

20 results Braking interventions off

21 results Braking interventions on

22 results Steering interventions on

23 NowLater Admissible set Safe set Models and estimates are always subject to uncertainty, we can account for: Uncertainty in state estimates Uncertainty in estimates of surrounding environment (e.g. curvature, friction..) Uncertainty in model parameters (e.g. driver model parameters) How about uncertainty?

24 Concluding remarks

25 Threat assessment algorithms –papers 1-4 Driver model estimation –paper 2 Uncertainty in estimates of state, additive disturbances, model parameters –paper 3 Nonlinear dynamics –papers 1 & 4 Decision making –paper 1 Intervention design –paper 1 Everything validated through experiments –papers 1-4 contributions

26 Safety feature that utilizes knowledge of the road geometry to: Avoid loss of control Keep the vehicle on the road Make the vehicle easier to maneuver Driver skills are not limiting Friction estimation is difficult in low excitation Curvature estimation is difficult on bad roads Driving application Vehicle dynamics control Collision avoidance systems

27 Thank you for listening! Don’t run off the road like I did!


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