IDRES: A rule-based system for driving IDRES: A rule-based system for driving situation recognition with uncertainty situation recognition with uncertainty

managementmanagement

J.M. Nigro and M. RombautJ.M. Nigro and M. Rombaut

20070927 Jun Ki Min20070927 Jun Ki Min

Information Fusion 패턴인식 시스템응용

OVERVIEWOVERVIEW

•Experimental Vehicle (EV)

•Manoeuvre Recognition in Driving Situation Context

•IDRES System– Confidence Evaluation– Belief Model

•Results

•Summary

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Experimental Vehicle (EV)Experimental Vehicle (EV)

•Driver’s assistance system must take into account high-level information Context aware

– The intent of the driver: Manoeuvre recognition + Driver behaviour model

•IDRES: Intelligent Driving Recognition with Expert System

– CASSICE project: Symbolic Characterization of Driving Situations– Determines the current manoeuvre performed by the driver– Rule-based system managing the uncertainty of data

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Manoeuvre Recognition Manoeuvre Recognition in Driving Situation Contextin Driving Situation Context

•The context of the driving situation– The state of the vehicle (speed, acceleration)– The position of the vehicle (on the road, in the lane)– The type of static environment (highway, number of lanes)– The state of the dynamic environment (the other vehicles)

•Manoeuvre Mj is an ordered sequence of states si

– Mj =(s1, s2, …, sN)

– State: Combination of conditions– Conditions: Evaluated from the sensors of the EV

•Overtaking manoeuvre

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IDRES SystemIDRES System

•Advice level– Uses the data from the sensors

to determine the states– Recognizes, at any time, which

states are in progress

•Sequence recognition level– Uses all the states found by the

advice-rules and the previous results– Selects the sequence that match

one of the priori defined sequence– Three principles

• Respect the order of thestates

• Some states may not berecognized

• Previous state: Invalid,Following state: Not yet valid Persist the previous state

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Manoeuvre Recognition with IDRESManoeuvre Recognition with IDRES

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IDRES System

Confidence Evaluation

•Numerical values of the inputs logical (symbolic) values

•Model the confidence of the logical data by a belief function

•A and B

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IDRES System

Belief Model

•State s is associated with a distribution of mass ms

•“Waiting for overtaking”

•Modified rules

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IDRES System

Results: End of PassingResults: End of Passing

•The rule End of Passing (s7)– c1 (Speed of EV > Speed of TV)

– c2 (EV on the left lane andTV on the right lane)

– c3 (TV behind EV)

•At time p2, IDRES is not certain thatEV manoeuvre is End of passing

– EV and TV are on the same lane certain: mc2(c2) = 1

– EV is faster than TV certain: mc1(c1) = 1

– TV is behind EV ? not certain: mc3(c3) = 0.5 and mc3(c3Uc3)= 0.5

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Results: Overtaking RecognitionResults: Overtaking Recognition

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SummarySummary

•Recognition of a sequence of states from numerical rough data taken from a real dynamic system

– Driving manoeuvre

•IDRES system– Two levels– Uncertainty management

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