A fledgling advanced lane management assist system can take the guesswork out of selecting the right lane on a congested freeway, as its inventor Robert Gordon explains.
As drivers we’ve all done it and control room staff see it all the time – motorists on congested freeways switching into what they perceive is a faster lane, only to come to a halt a few moments later and watch vehicles in the other lanes continue to move past. Now, by re-analysing readily available data in an advanced lane management as
Figure 1: Flow Rate (Veh/h/ln); Figure 2: Speed above the speed limit SASL (MPH)
A fledgling advanced lane management assist system can take the guesswork out of selecting the right lane on a congested freeway, as its inventor Robert Gordon explains.
As drivers we’ve all done it and control room staff see it all the time – motorists on congested freeways switching into what they perceive is a faster lane, only to come to a halt a few moments later and watch vehicles in the other lanes continue to move past. Now, by re-analysing readily available data in an advanced lane management assist (ALMA) system, there can be a definitive answer the motorists’ most vexing question: Is it worth changing lanes?
Motorists driving conventionally operated vehicles enter the freeway, merge into the mainline and select a lane. However, their choice is often suboptimal or even poor because they have no knowledge of the downstream traffic conditions in their selected lane or the alternative lanes. The result is often lane switching which does not accomplish the driver’s objective – normally increasing speed - and after a short distance results in travelling at the same speed or even lower speed than vehicles in the previous lane. Not only is this type of lane switching unproductive, it causes driver aggravation, disturbances to traffic flow and wastes fuel, it also results in more accidents (4% of crashes in the US occur during lane changes).
Consider the same situation with automated vehicles which also select a lane on the freeway but do so using a host of information. Currently such vehicles obtain commercially available routing information based on traffic and mapping for route selection, while vehicle-mounted sensors guide or control the longitudinal and lateral movements including lane changes. In the future additional data may be available from nearby vehicles via vehicle-to-vehicle communications to supplement the data from the on-board sensors.
The routing information is based on general traffic conditions and while the driver can usually request driving preferences such as the quickest route, this does not extend to lane related speed information. Both the other information sources enable a lane change to be safely executed but none meaningfully address the issue of whether a lane change will shorten the journey time or provide a more comfortable trip.
As freeway traffic volumes build towards capacity, the flow begins to break down and become unstable (Figure 1) so speed in each lane may vary considerably with distance and time. While drivers can assess the situation in adjacent lanes, they cannot see conditions five minutes down the road so lane changes, particularly those of the more aggressive drivers, often accomplish very little. An example from studies examining how motorists choose their desired target speed based on their habits and preferences is shown in Figure 2. It can be seen that approximately 25% of the vehicles travel at or below the speed limit in this example.
Traffic management centres (TMCs) utilise sensors such as inductive loops, radar and acoustic detectors, magnetometers and video processing to monitor traffic conditions by lane. With appropriate processing this data could be used to provide ‘look-ahead’ information to give drivers a more complete picture. For example, if a vehicle is in lane two and those in lane three are visually moving faster, in the absence of any other information the driver might decide to change lanes. However, if drivers had information showing that over the next 5km (3 miles) the average speeds are almost identical, they would probably not bother changing lanes.
Most TMCs have data feeds giving traffic volume and speed in each lane and this information could be used to compute average headway by lane – information that is not readily obtainable using vehicle-to-vehicle communication. TMCs also have other information sources including CCTV, road-weather sensors, police information, 911 and service patrols; but generally this is not in a form motorists could use for lane selection. To be of use the information must be conditioned to convey lane selection and target speed in a useable format and be available at the decision points on the roadway to which it relates.
In addition to traffic conditions, other factors affecting lane selection include vehicle type and use (truck restrictions and high occupancy vehicles), toll and toll tag requirements, distance to required exit and lane closures.
Over recent years freeway TMCs have started using active traffic management (ATM) strategies to essentially alter lane use regulations to accommodate current traffic conditions. Other ATM strategies change with time or traffic conditions such as changes in speed limits (and possibly automatic enforcement) and the use of the shoulder for normal traffic flow. While such ATM strategies are beneficial they increase the decision-making requirements for drivers of automated and conventional vehicles alike.
An advanced lane management assist (ALMA) system conveys useful information to vehicles in advance of actually encountering these conditions to facilitate more strategic lane selection, particularly when combinations of traffic management strategies are employed. At a conceptual level ALMA may be viewed as decision support software which lies between the vehicle’s navigation system (positioning and route selection) and the lateral/longitudinal control functions. This patent pending approach is shown in Figure 3.
The ALMA management centre obtains information from the freeway TMCs and processes the data to calculate for each lane the speed, volume and density of traffic, the average vehicle length, the gap between vehicles and the passenger car equivalent volume. Figure 4 shows the principal data flow relationships among ALMA modules, the TMC and the vehicle.
For conventional vehicles, ALMA may be considered an extension of the navigation system to the lane guidance level. It advises motorists of a preferred lane and a target speed for that lane, however the motorist makes the decision and performs the corresponding manoeuvres. ALMA provides a strategy enabling automated vehicles to select a lane while their lateral and longitudinal controls perform the actual lane changing manoeuvres and associated safety functions.
The ALMA information incorporate dynamic, static and user-selected lane use requirements and restrictions such as lane closures, variable speed limits, hard shoulder running, weight restrictions, HOV and toll/tag requirements.
All such information must be keyed to the decision points that are influenced by geometrics, routes, the likely availability of a suitable gap in the traffic in an alternative lane, TMC imposed lane management, vehicle classification, number of occupants and toll facility preferences. This information enable the selection of an appropriate lane and target speed based on the driver’s preferences, vehicle parameters and a set of decision rules established in the vehicle.
The ALMA management centre requires information generated by state/agency-run TMCs and these use a variety of different software architectures, data structures and field equipment deployment styles. To cope with this the ALMA management centre adapts to each TMC’s output and provides information to a Guidance Assist Vehicle Module using ALMA data structure formats.
ALMA’s data structures combine the TMC derived information with that relating to the vehicle, driver and occupancy information, allowing the Guidance Assist system to develop recommended lane change strategies and target speeds for the selected lane. This information can be sent to the vehicle via cellular, satellite or cloud-based communication networks and, where available, ALMA can also utilise connected vehicle communication technology.
This strategy takes into account the motorist’s driving speed preferences, the class of vehicle, number of passengers, exit proximity, tolling preferences and the likely availability of a suitable gap in the traffic in an alternative lane to attempt a merge. With conventional vehicles the driver performs the lane change whereas automated vehicles utilise their lateral and longitudinal control systems.
But before it will recommend (or in the case of an automated vehicle, initiate) a lane change, the guidance assist module requires a degree of long-term speed differentiation between the current and other lanes for the next few kilometers. It must also detect the likelihood of a suitable gap in the traffic in the alternative lane.
So, for example, if the vehicle is currently in lane two and the average speed in lane three is 3kmh (2mph) higher for the next 5km (three miles) then a lane change would not be advised. Lane change indications are limited to where they will work meaningfully and the lack of any lane-change advice provides drivers with the assurance that they will not progress more slowly if they remain in their current lane. Where the system detects that there is little difference in relative lane speeds such information could also be conveyed to all drivers by variable message signs. By avoiding over-corrections as multiple drivers swap lanes, the traffic flow will tend to be more stable and the trip quality improved while also reducing the potential for accidents during lane changing.
Using data already available to many TMC along with information from the vehicle and its driver, the ALMA approach provides a superior way to select the most appropriate lane and target speed for both conventional and automated vehicles.
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Robert Gordon is a consultant and author with extensive experience in the functional design and software requirements of freeway management and traffic signal systems and has conducted research projects for the831 Federal Highway Administration (FHWA) and National Cooperative Highway Research Program (NCHRP).
As drivers we’ve all done it and control room staff see it all the time – motorists on congested freeways switching into what they perceive is a faster lane, only to come to a halt a few moments later and watch vehicles in the other lanes continue to move past. Now, by re-analysing readily available data in an advanced lane management assist (ALMA) system, there can be a definitive answer the motorists’ most vexing question: Is it worth changing lanes?
Motorists driving conventionally operated vehicles enter the freeway, merge into the mainline and select a lane. However, their choice is often suboptimal or even poor because they have no knowledge of the downstream traffic conditions in their selected lane or the alternative lanes. The result is often lane switching which does not accomplish the driver’s objective – normally increasing speed - and after a short distance results in travelling at the same speed or even lower speed than vehicles in the previous lane. Not only is this type of lane switching unproductive, it causes driver aggravation, disturbances to traffic flow and wastes fuel, it also results in more accidents (4% of crashes in the US occur during lane changes).
Consider the same situation with automated vehicles which also select a lane on the freeway but do so using a host of information. Currently such vehicles obtain commercially available routing information based on traffic and mapping for route selection, while vehicle-mounted sensors guide or control the longitudinal and lateral movements including lane changes. In the future additional data may be available from nearby vehicles via vehicle-to-vehicle communications to supplement the data from the on-board sensors.
The routing information is based on general traffic conditions and while the driver can usually request driving preferences such as the quickest route, this does not extend to lane related speed information. Both the other information sources enable a lane change to be safely executed but none meaningfully address the issue of whether a lane change will shorten the journey time or provide a more comfortable trip.
As freeway traffic volumes build towards capacity, the flow begins to break down and become unstable (Figure 1) so speed in each lane may vary considerably with distance and time. While drivers can assess the situation in adjacent lanes, they cannot see conditions five minutes down the road so lane changes, particularly those of the more aggressive drivers, often accomplish very little. An example from studies examining how motorists choose their desired target speed based on their habits and preferences is shown in Figure 2. It can be seen that approximately 25% of the vehicles travel at or below the speed limit in this example.
Traffic management centres (TMCs) utilise sensors such as inductive loops, radar and acoustic detectors, magnetometers and video processing to monitor traffic conditions by lane. With appropriate processing this data could be used to provide ‘look-ahead’ information to give drivers a more complete picture. For example, if a vehicle is in lane two and those in lane three are visually moving faster, in the absence of any other information the driver might decide to change lanes. However, if drivers had information showing that over the next 5km (3 miles) the average speeds are almost identical, they would probably not bother changing lanes.
Most TMCs have data feeds giving traffic volume and speed in each lane and this information could be used to compute average headway by lane – information that is not readily obtainable using vehicle-to-vehicle communication. TMCs also have other information sources including CCTV, road-weather sensors, police information, 911 and service patrols; but generally this is not in a form motorists could use for lane selection. To be of use the information must be conditioned to convey lane selection and target speed in a useable format and be available at the decision points on the roadway to which it relates.
In addition to traffic conditions, other factors affecting lane selection include vehicle type and use (truck restrictions and high occupancy vehicles), toll and toll tag requirements, distance to required exit and lane closures.
Over recent years freeway TMCs have started using active traffic management (ATM) strategies to essentially alter lane use regulations to accommodate current traffic conditions. Other ATM strategies change with time or traffic conditions such as changes in speed limits (and possibly automatic enforcement) and the use of the shoulder for normal traffic flow. While such ATM strategies are beneficial they increase the decision-making requirements for drivers of automated and conventional vehicles alike.
An advanced lane management assist (ALMA) system conveys useful information to vehicles in advance of actually encountering these conditions to facilitate more strategic lane selection, particularly when combinations of traffic management strategies are employed. At a conceptual level ALMA may be viewed as decision support software which lies between the vehicle’s navigation system (positioning and route selection) and the lateral/longitudinal control functions. This patent pending approach is shown in Figure 3.
The ALMA management centre obtains information from the freeway TMCs and processes the data to calculate for each lane the speed, volume and density of traffic, the average vehicle length, the gap between vehicles and the passenger car equivalent volume. Figure 4 shows the principal data flow relationships among ALMA modules, the TMC and the vehicle.
For conventional vehicles, ALMA may be considered an extension of the navigation system to the lane guidance level. It advises motorists of a preferred lane and a target speed for that lane, however the motorist makes the decision and performs the corresponding manoeuvres. ALMA provides a strategy enabling automated vehicles to select a lane while their lateral and longitudinal controls perform the actual lane changing manoeuvres and associated safety functions.
The ALMA information incorporate dynamic, static and user-selected lane use requirements and restrictions such as lane closures, variable speed limits, hard shoulder running, weight restrictions, HOV and toll/tag requirements.
All such information must be keyed to the decision points that are influenced by geometrics, routes, the likely availability of a suitable gap in the traffic in an alternative lane, TMC imposed lane management, vehicle classification, number of occupants and toll facility preferences. This information enable the selection of an appropriate lane and target speed based on the driver’s preferences, vehicle parameters and a set of decision rules established in the vehicle.
The ALMA management centre requires information generated by state/agency-run TMCs and these use a variety of different software architectures, data structures and field equipment deployment styles. To cope with this the ALMA management centre adapts to each TMC’s output and provides information to a Guidance Assist Vehicle Module using ALMA data structure formats.
ALMA’s data structures combine the TMC derived information with that relating to the vehicle, driver and occupancy information, allowing the Guidance Assist system to develop recommended lane change strategies and target speeds for the selected lane. This information can be sent to the vehicle via cellular, satellite or cloud-based communication networks and, where available, ALMA can also utilise connected vehicle communication technology.
This strategy takes into account the motorist’s driving speed preferences, the class of vehicle, number of passengers, exit proximity, tolling preferences and the likely availability of a suitable gap in the traffic in an alternative lane to attempt a merge. With conventional vehicles the driver performs the lane change whereas automated vehicles utilise their lateral and longitudinal control systems.
But before it will recommend (or in the case of an automated vehicle, initiate) a lane change, the guidance assist module requires a degree of long-term speed differentiation between the current and other lanes for the next few kilometers. It must also detect the likelihood of a suitable gap in the traffic in the alternative lane.
So, for example, if the vehicle is currently in lane two and the average speed in lane three is 3kmh (2mph) higher for the next 5km (three miles) then a lane change would not be advised. Lane change indications are limited to where they will work meaningfully and the lack of any lane-change advice provides drivers with the assurance that they will not progress more slowly if they remain in their current lane. Where the system detects that there is little difference in relative lane speeds such information could also be conveyed to all drivers by variable message signs. By avoiding over-corrections as multiple drivers swap lanes, the traffic flow will tend to be more stable and the trip quality improved while also reducing the potential for accidents during lane changing.
Using data already available to many TMC along with information from the vehicle and its driver, the ALMA approach provides a superior way to select the most appropriate lane and target speed for both conventional and automated vehicles.
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Robert Gordon is a consultant and author with extensive experience in the functional design and software requirements of freeway management and traffic signal systems and has conducted research projects for the
