In an automated vehicle there are different external factors that could affect the passenger state, which are defined as completely external to the driver and the vehicle. A first approach to define and classify the external factors for the autonomous driving is found in Adaptive project (FP7-ICT-2013.6.5: Co-operative mobility Grant Agreement No. 610428) in D2.1. System Classification and Glossary.
The classification of the environment factors made by Adaptive is used as a basis for SUaaVE approach and for the development of ALFRED (a concept to humanise the behaviour of the vehicle according to the passenger state). Additionally, manoeuvre factors defined by Adaptive related with the interaction with other vehicles has been included.
Following Adaptive project, external factors aredividedin four categories: Traffic, Road, Visibility and Maneuvers.
1. TRAFFIC PARAMETERS
The main parameters to classify the traffic factors are:
.- Traffic participants (type of road users in the environment). These can be non-motorized road users and motorised road users.
.- Mix of participant according with automatization (defines whether the environment is shared with or without automated vehicles).
.- Traffic flow (which mixes the concepts of traffic speed and density).
- Moving traffic: Traffic is moving nearly with recommended speed of particular road type. Traffic density is low or medium.
- Slow moving traffic: Traffic is moving distinctly below recommended speed of particular road type. Traffic density is medium to high.
- Stationary traffic: Traffic is nearly at a standstill or is at a standstill. Traffic density is high.
2. ROAD PARAMETERS
The main parameters to classify the road characteristics are:
- Road condition. This factor describes the road according with smoothness and adhesion conditions (good, slippery and bumpy).
- Road accessibility. This factor characterizes privacy of the road (public or private).
- Road geometry. This factor synthesizes the geometry of the road in terms of curves and slopes (straight, curved or steep).
- Road infrastructure. This synthesizes the different elements in the road.
- Physical cut-off: Physical cut-off between oncoming lanes. Example: Guardrail, separating green area.
- Good lane markings: White / yellow painted stripes or botts’ dots to separate lanes of a road.
- Guard rails: Mechanical construction to prevent vehicles from veering off the roadway into oncoming traffic, crashing against solid objects or falling into a ravine. Examples: Guard rails, mural, concrete wall, taut steel rope, mound
- Deer fences: Fence at the roadside which prevents animals and pedestrians from entering the road. Remark: “No deer fence” does not mean “no automation”. The evaluation of minimal infrastructure requirements for specific applications is a separate topic. Example: A Traffic Jam Pilot might not need a deer fence. For high speed application is has to be assessed if occurrence probability of deer in combination with perception performance results in an acceptable risk.
- Emergency lanes: Separate lane at the roadside which is reserved for vehicles with technical defects. Remark: Hard shoulders is a synonym for emergency lane
- Traffic light: Traffic light at intersections of e.g. urban or rural roads.
- Road type. Main road types are: Motorway, Highway, Interstate, Rural road, Arterial road, Urban road, Residential district roads, Parking area & parking deck and Garage.
3. VISIBILITY DUE TO EXTERNAL CONDITIONS PARAMETERS
Visibility factors are included in SUaaVE approach to define easiness of visibility of environment by the passengers inside the car due to the external conditions. These are:
.- Good visibility: Full visibility of vehicles and obstacles. Remark: Modest fog, spray, rain or snow shall not hamper system functionality.
.- Poor visibility due to obstacles (vehicles or infrastructure).
.- Poor visibility due to weather conditions (fog, heavy spray, heavy rain, heavy snow).
4. EXTERNAL PARAMETERS OF MANEUVER (RELATED WITH OTHER VEHICLES)
In Adaptive, classification vehicle maneuver is characterized by the following parameters: maneuver time to collision, maneuver duration, maneuver automation, maneuver speed range, maneuver control force, maneuver time headway, maneuver trigger and maneuver coordination.
For external factors characterization, SUaaVE considers only the parameters related with maneuver in relation with the interaction with other vehicles in the road, i.e. maneuver time to collision, maneuver time headway and maneuver coordination. Maneuver duration, maneuver automation, maneuver speed range, maneuver control force and maneuver trigger are included in dynamic comfort parameters (Ego Vehicle factors).
- Vehicle manoeuvre time-to-collision. This factor characterizes if the collision is imminent or not.
- Large: Collision is not imminent. Example: Driver assistance systems such as ACC, LKA, etc.
- Small: Collision is imminent. Example: emergency braking e.g. if lead vehicle brakes hard suddenly.
- Vehicle manoeuvre time headway: This factor characterizes the in seconds the distances between vehicles.
- Standard: Time headway > 0,9 sec Examples: ACC, Traffic Jam Assistance.
- Reduced: Time headway 0,5 … 0,9 sec Example: truck platooning with 15m distance.
- Small: Time headway < 0,5 sec Example: truck platooning with 5m distance.
- Vehicle manoeuvre coordination: This factor characterizes the coordination nature of the manoeuvre.
- With coordination: Manoeuvre involves several vehicles which are coordinating their behaviour. Example: Automated filtering at on-ramp of a motorway – vehicle that wants to enter motorway asks vehicles on motorway via V2V communication to increase headway so to ease filter-in manoeuvre.
- Without coordination: Manoeuvre is not coordinated between involved vehicles. Example: Lane change at overtaking manoeuvre – if the adjacent lane is not occupied the lane change is initiated without any coordination or communication between involved vehicles.
For more information about the external factors that could influence the passenger’s state, please see Deliverable 3.1. Framework of the emphatic module and preliminary relationship among automotive factors with cognitive and emotional passenger state.