DAVI Pr ogress & Ambitions

1. DAVI Progress & Ambitions Dutch Automated Vehicle Initiative www.davi.connekt.nl R. Happee r.happee@tudelft.nl

2. Dutch Automated Vehicle Initiative To investigate, improve and demonstrate automated driving on public roads • Assess & improve technology • Study human behaviour • Prove safety • Pursue legalisation • Create public awareness

3. DAVI in time • Inception March 2013 • Official Launch Innovatie-estafette Nov 2013 • Experiments with non-expert drivers 2015 • Demo with non-expert drivers 2016

4. key projects & partners

5. Hands free • Cruise Control with automated steering • Highway in 2020 • Capable driver Autonomous • No steer & pedals • User only has to select the destination Mixed with normal traffic

6. Vehicles • 3 Priuses TNO / DITCM • CACC • Automated steering • Prius Fontys • Automated steering • 2 Priuses TU Delft • Towards 360 degsensing

7. Ambitions • Vehicle automation • Robust environment perception • Safe control strategies • Link automation to traffic management • Human factors • Transitions of control • Acceptance, trust, use & misuse • Interaction with other road users • Benefit prediction • Legalisation • Public awareness & acceptance

8. Environment Perception • Cameras, Radar, Ultrasound, Lidar • Cars, trucks, pedestrians, cyclists • Road markings • Road Signs • Traffic lights • Challenges • Reliable detection in various weather conditions • Intent recognition & motion prediction • Integration environment sensing with communication and detailed maps

9. Custom of the shelf sensors HDR vision systems(Melexis) ARS 309-2 HS radars (Continental) Main Processing Unit PXIe-1062Q RTOS (NI) High accuracy Digital maps & eHorizon systems (Mapscape – Maccom - Electrobit)

10. Graceful degradation of CACC • ACC: no V2V communication • CACC: full V2V communication • Graceful degradation in case of packet loss: dCACC 5 4 3 2 1 CACC ACC dCACC

11. Automated lane changing & merging • Shown in movies carmakers • Proof of safety not found • DAVI simulation studies • Optimize gap & timing1 • Optimize collective benefits1 • Trajectory control with non-linear vehicle model2 • Next • Sensors fusion & inaccuracy • Role of the driver? • Wang M, Hoogendoorn SP, Daamen W, van Arem B, Happee R. Novelpredictive approach forunifiedlane-changingandcar-followingcontrol. Submitted ISTT21 • Gottardis P, Manazza SS. Automated Controlled Vehicle Based on Non-Linear Model Predictive Control Connected to a Safety Path Planner with Online Collision Risk EstimationMSc thesis TUDelft/ Polimi 3 Oct 2014

12. Human Factors • Literature survey1 • Extensive simulator studies Highly Automated Driving • Workload affects HAD much larger than ACC • Only expert drivers tested with HAD on public roads (3 studies) • Challenges • Monitor & Manage driver awareness • Develop safe transition procedures & interfaces • Investigate human interaction with HAD on public roads • de Winter JCF, Happee R, Martens MA, Stanton NA. (2014). Effects of ACC and highly automated driving on workload and situation awareness: A review of the empirical evidence. TRPF: Traffic Psychology and Behaviour.

13. Opinions on automated driving 1 • Diverse / extreme responses • 22% unwilling to pay for fully automated driving • 5% willing to pay more than $ 30,000 • 69% estimated fully automated driving to reach 50% market share before 2050. • concerned about • software hacking/misuse • legal issues and safety • Kyriakidis M, Happee R, de Winter JCF. Public opinion on automated driving: Results of an international questionnaire among 5,000 respondents. Submitted.

14. Automated Transport Foodvalley(Gelderland) • “De eerstezelfsturende auto’s van Nederland moetengaanrijden in Ede enWageningen” (Gelderlander 1 Oct 14). • Planstudie, suitable routes, sensors & control • Innovation • From closed track to public roads • Safe interaction with pedestrians, cyclists, cars • On demand public transport • Safety by • Low speed • Conservative control strategies • Control room monitoring

15. Milestones testing automation on public roads • Technical safety assessment • Sensing reliability • Safe operating conditions • Automation linked to traffic management • Behaviour non-expert drivers investigated • Safe transitions of control • Quantify how drivers will use automation • Prove safety taking into account the human interaction • Benefits predicted with traffic flow models • Legalisation

16. Questions

17. DAVI Partnership Charter • DAVI aims to • implement automated vehicles, • obtain permission for testing on public roads, • link vehicle automation to traffic management, • assess and improve automation technology, • study human behaviour with automation, • prove safety, • optimise transport efficiency, • create public awareness, • and pursue legalisation of automated driving. • DAVI pursues these objectives through complimentary research projects and demonstration activities with varying partnerships and national and international funding sources.

18. Vacancies • Vacancy 1 (PhD Candidate) Human interaction with automation • Human machine interface, driver state monitor • Investigate the human interaction with automation • Vacancy 2 (PhD Candidate) Benefits & risks of automated driving • Develop computer models capturing mixed traffic • Predict & optimise benefits • Vacancy 3 (Post Doc) Automation & safety assessment • Automation algorithms, merging, lane changing and overtaking • Safety assessment methods • Vacancy 4 (Engineer) Environment sensing • Systems architecture • Sensors & fusion providing robust 360 degree environment perception