Reducing randomness: the advent of self driving cars
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1 Reducing randomness: the advent of self driving cars Prof Dr Ir Bart van Arem TU Delft, Faculty of Civil Engineering and Geosciences Department Transport & Planning 1
2 Your speaker MSc, Qeueing models at intersections, UT PhD Queueing models for slotted transmission systems, UT 1991 Modelling traffic at roundabouts, Rijkswaterstaat Modelling intelligent transport systems, TNO Part-time professor Driver support system UT Professor Transport Modelling, TU Delft H-index 17 i10 index 29 5 PhDs 60 MSc s 2
3 3
4 Reducing randomness Road traffic flow strongly heterogeneous and stochastic First moment of delay depends on second moment of inter arrival times Randomness of driver/vehicle behaviour in terms of anticipation, speed, reaction time, acceleration Can self driving vehicles reduce randomness and improve traffic flow efficiency? 4
5 5
6 What is automated driving? Partial automation Available, Mercedes S class Limited scope High automation Massive worldwide R&D Full automation Decades away unless on dedicated infrastructure or driving very slowly 6
7 7
8 Potential impacts Solve traffic jams by increased outflow No accidents (?) Non connected Large penetration Prevent traffic jams by better stability Better distribution of traffic over network Decreased throughput by larger headways Decreased stability by lack of anticipation Less congestion delay Better energy efficiency Increased risk of congestion? 8
9 The congestion assistant Detects downstream congestion Visual and auditive warning starting at 5 km before congestion Active gas pedal at 1,5 km to smoothly slow down Takes over longitudinal driving task during congestion 9
10 Impacts on driving behaviour Motorway scenario with congestion Impacts on driving behaviour Acceptance 10
11 Effects on mean speed 11
12 Effects on time headway May 31,
13 ITS Modeller Driver model Vehicle model Comm model Sensor model Actutator model Exchange layer ITS Modeller Paramics Vissim Aimsun 13
14 Longitudinaal bestuurdersmodel a ref _ v = r ( v v) ref a ref _ d = c d ( d( t t ) d ) + c v ( t t ) + c v ( t t ) r ref v _ p rel _ p r v _ pp rel _ pp r d ref = c c2 v + c3 v 13 November
15 Study area: merging area A12 motorway, Woerden, the Netherlands start end upstream detector downstream detector 4.1 km 2.1 km 15
16 Calibratie Speed (km/h) Speed (km/h) Speed upstream 15:00 15:30 16:00 16:30 17:00 0 Time Speed downstream 15:00 15:30 16:00 16:30 17:00 Time A12 ITS Modeller A12 ITS Modeller 13 November
17 Model file assistent Actief gaspedaal: Stop & Go a ac 2 2 v j v = 2 x d a st st _ v = d 0 + t st v = rst int ( v v) a st _ d = k a ( ( ) ) k d d + k v d st v rel _ p 17
18 Resultaten 120 Speed upstream - 10% CA Speed (km/h) Reference 1500 m 500 m 1.0 s 0.8 s Time (min) Speed upstream - 50% CA Speed (km/h) Reference 1500 m 500 m 1.0 s 0.8 s Time (min) 18
19 Resultaten-2 Travel time (min) Delay (min) Delay reduction Free flow (110 km/h) Reference m / 0.8 s (10%) % 500 m / 0.8 s (50%) % 19
20 Challenges Sensing and control Localization and positioning Communication networks Closing the circle Human factors: Behavioral adaptation, Authority transitions Traffic management Transport system design Travel patterns Multi-scale modelling 20
21 Lessons from Erik Always careful, precise Straight feedback Committment Humor Heen en weer blocnote Right drink at the right time 21
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