Towards Automatic Driving Xiaokai He – January 20 th, 2010

Outline Introduction for Automatic Vehicles from Technical Aspect Proposal of My Master Thesis – Optimized Lane Assignment in Urban environment

History Development of Driver Assistant Systems – Step by step introduction from driver assistance Requirement Technical feasibility Acceptance – ABS has already take over the driver’s control ABS 1978 ESP 1995 PRE-SAFE 2002

Current Driver Assistant Systems PRE-SAFE – two versions – From 2005, constituted in an S-Class (W221) – Combination with Distronic – Distance Keeping with Radar – Capture possible collision accidents through comparison between the speeds of each vehicles – Reaction in steps, timely depend on computed accident time points 1.(t-2.6s): acoustic/optic warning to the driver 2.(t-1.6s): automatic braking with 40% brake power 3.(t-0.6s): full brake power – driver can not take over the system 4.(t-0.1s): activate the safety belt, seat control, head support, windows…

Research Towards Total Automatic Vehicles Prometheus Project ( ) – The biggest European research project for automatic vehicles (till now) – Highway/freeway as goal area, human driver for safe concern – Up to 1700km distance, up to 180 km/h, longest distance without human control 158 km

Research Towards Total Automatic Vehicles DRAPA Grand Challenges – DARPA = Defense Advanced Research Projects Agency – Military applications from automatic vehicles (e.g. exploration, search and rescue, wardership) – Grand Challenge I – km Off-Road 10 Hours Total automatic navigation 1 Million $ Prize 15 participants – mostly US University No Team has reached the Goal The best team went 12 KM

Research Towards Total Automatic Vehicles DRAPA Grand Challenges – Grand Challenge II – km Off-Road 10 Hours Total automatic navigation 1 Million $ Prize 23 participants – mostly US University 5 Teams have reached the Goal 22 teams has surpassed best of the last year Winner (Stanford) has finished the 240km in 6 hours and 54 minutes (~34km/h)

Research Towards Total Automatic Vehicles DRAPA Grand Challenges – Grand Challenge III – km on a modified street system 6 hours Total automatic navigation Following the traffic rules 1 Million $ Prize 35 participants includes international competitors 6 Teams have reached the Goal

Research Towards Total Automatic Vehicles PATH – The California Program on Advanced Technology for the Highway ( Now) – Traffic Operations Research Traffic management Traveler information – Transportation Safety Research – Modal Applications Research

Used Driving Areas Highway (Prometheus) Desert/Off-Road (DARPA I&II) Modified City-Scenario (DARPA III) – Clear defined environment – Countable situations – Clearly recognizable environment Other situations also imaginable, e.g. car park

Expected Driving Areas Where the drivers want: – Boredom – Overextension Where the lives can be protected: – In serious dangerous situation – Seconds before a accident Where human is in continuing danger – Crisis area – Catastrophic area Earthquake Radiate area

Definition A Vehicle, which automatic drives and – Goes after the Goal complying the rules – Perceives, interprets the environment and reaction in an appropriable way

Definition Communication is the Key of effective automatic driving

Sensors Input for Situation Analyze Transfer raw data Generally: – Radar (near/far) – Infra – Ultrasound – Image level Camera IR-Camera (night sight) Stereo-Camera (for 3D information) – GPS We also have: – LIDAR (Laser scanner) Punctual environment information

Sensor Evaluation Sensors collect only raw data – Pixel from Cameras – Voltage from IR/Ultrasound – Distance from Radar – 3D Pixel from LIDAR These Data must be evaluated – Pixel-Clustering – Object recognition/identification – Muster recognition

World Model Combination and Summary of the evaluated Sensor data Situation Analyze Compare with the previous data Translate as real word Categorizing in related concepts

Strategy Long-term plan of the route Similar to navigation system Updates in real time Translate e.g. through Rule-Engine

Tactic Short-term plan Transfer the Strategy to effective route plan Change the lane plan according to situations – Normal driving – Overtaking control – Avoidance control – Turn off procedure Control and correction through actuators according to new sensor condition (loop control)

Goals Pretended Goals of the Vechicle For example – Drive a circuit – Drive to Peking – Optimize the fuel consumption – Avoid traffic jam Impact on strategy and sometime also on tactic decisions

Rule Rules, which the vehicle must follow Street traffic rules Test restrictions Can be overrode: avoiding accident with human is more important than stay in the lane Impact on tactic and sometime also on strategy decisions

Actuators Control the vehicle Gas/Brake Steering Blinker Cockpit-Electronic Drive-by-Wire Additional actuators (e.g. warn siren)

Communication (Evaluated) Sensor data share with other vehicles in the environment – position, speed and direction Cooperatively route plan – avoiding traffic congestion, environment pressure, shorten the drive time Broadened sensor sharing – part of the reconstructed information All the vehicles in a specific area share a local map with dynamic information Cooperatively lane plan to avoid accidents and increase the traffic efficiency

Outlook Research has solved part of the full automatic driving problem Further work needed for sensor evaluation and lane planning Extend the communication between full automatic and human controlled vehicles Deploy the automatic vehicles in restricted areas – Highway – Garage – In dangerous situations For “real” full automatic vehicles, we need not only research, but also impulse from politic, the vehicle manufactures and making dedicated laws

Proposal of Master Thesis Optimized Lane Assignment in Urban Environment – Goal – Scope – State of the art – Assumptions – Challenges

Goal Build a centralized control system that assign an appropriate lane for each vehicle on each road Provide a navigation algorithm to cooperate with the lane allocation to achieve a maximal throughput using the existent facilities

Scope Construct a centralized control system to provide the lane assignment for each vehicle Provide lane assignment algorithms in dynamic urban area and evaluate with simulation Provide advanced navigation/routing algorithms to utilize the benefits from the lane assignment Evaluate the system with certain metrics to prove the improvement of the global traffic efficiency Out of Scope – Vehicle Maneuver (lane change/keeping) – Coordination between vehicles

State of the art Lane assignment is well studied under highway/freeway scenario – Platooning – Economic methods Comprehensive Vehicle Models Dynamic routing algorithms

Main Assumptions One general vehicle model – Different vehicle model for different vehicle types: car, bus, truck… 100% penetrate rate – System behavior under different penetrate rate is important Controller knows all the necessary information: – For each vehicle: destination… – For each road: capacity, traffic… Unrestricted communication capability – Latency, capacity, overhead need to be take in consideration – Thus different communication technologies need to be utilized One central Controller – Using the intelligence of the vehicles

Challenges Modulation of the complex urban area – Different scenarios (single road, intersection, elevator…) – Micro & Macro scope Cooperate with other road facilities (traffic lights) Metric of Efficiency Fairness (who want to join the slow lane?) Safety

Idea Collection Thank you for your attention! ? ? || /* */