Primary operating owner
Head ADAS; Vehicle Safety; Road Safety
P26 · Representative solution case · Automotive / Government / Insurance
ADAS, HD Maps & Road Safety Intelligence
What safety decisions could improve if the vehicle knew the road beyond sensor range?
Evidence distinction: this is a representative solution case constructed from the source requirements and Mappls delivery pattern. It is not presented as a named-customer outcome unless a separate verified customer story is explicitly linked.
01 · Operating context
The decision environment and the problem to change
Camera/sensor-only systems lack horizon context and consistent road intelligence, while fleets and agencies need actionable risk maps.
Economic decision
CTO; VP Engineering; Transport/Road Safety Secretary
Technical influence
ADAS Algorithm; Map Validation; Functional Safety
Why action becomes urgent
ADAS programme, NCAP/safety target, accident audit, speed-limit compliance, road-safety MoU, insurance telematics.
02 · Before and after
Change the operating model, not only the interface
Fragmented context and delayed action
Camera/sensor-only systems lack horizon context and consistent road intelligence, while fleets and agencies need actionable risk maps.
- Location, operational and enterprise data remain separated
- Stakeholders make inconsistent decisions from partial context
- Exceptions are found late and evidence is difficult to reconstruct
A governed location-aware decision loop
HD/lane and road-attribute maps, speed limits, curvature, hazards, accident blackspots, predictive horizon, safety alerts and validation services.
- One role-aware workspace in Mappls Navigation Lab
- Live context moves directly into the responsible workflow
- Every decision, scenario and outcome can be measured and audited
03 · Stakeholder experience
One shared system, different role moments
Own and govern
Head ADAS
Own and govern
Vehicle Safety
Decide and coordinate
Road Safety
Decide and coordinate
CTO
Decide and coordinate
VP Engineering
Execute and verify
Transport/Road Safety Secretary
Execute and verify
ADAS Algorithm
Execute and verify
Map Validation
Inside Mappls Pro, the same user can hold this role here and a different role in another application. Membership, saved runs and administration remain app-specific.
04 · Day-in-the-life journey
How the solution turns information into an operational result
Frame the operating decision
Which ADAS functions and ODD are targeted? What accuracy/freshness thresholds apply? How are false positives measured?
Connect the location foundation
Bring the required map, address, road, place, imagery, asset, customer and operational data into a governed Navigation workspace.
Act through role workflows
Route lab, Guidance simulator, Traffic scenarios, Road-event replay give each stakeholder the information, decision and action appropriate to their role.
Prove the intervention
Representative corridor pilot comparing map horizon and safety attributes with vehicle/sensor observations.
Scale and continuously improve
Operationalise the model through Data licence/royalty + validation services + update subscription., adoption governance, service measurement and a managed improvement backlog.
05 · Solution and data architecture
The Mappls capabilities behind the experience
Location foundation
Map, address, place, route, imagery, boundary and context layers
Operational context
Customer, asset, order, sensor, incident, task or transaction records
Application workflow
Route lab · Guidance simulator · Traffic scenarios · Road-event replay
Outcome loop
Attribute accuracy and freshness accepted; alert precision/recall threshold met; integration latency and coverage proven.
ADAS/HD & Safety Data
Lane, road attribute, risk and predictive horizon data
Product detailsTraffic, ETA & Road Events
Live/historical traffic, ETA and safety/road events
Product detailsAsset & Personal Trackers
Battery/portable/asset location and alert devices
Product detailsRealView 360 & Ground Imagery
Geotagged 360 imagery, photos and videos
Product details06 · Outcome and evidence
Make the value claim measurable before scaling
Bounded proof
Representative corridor pilot comparing map horizon and safety attributes with vehicle/sensor observations.
- Confirm the baseline and decision scope: Which ADAS functions and ODD are targeted? What accuracy/freshness thresholds apply? How are false positives measured?
- Use representative and, where approved, customer data in the configured workspace
- Review measurable success with the operating and economic owners: Attribute accuracy and freshness accepted; alert precision/recall threshold met; integration latency and coverage proven.
- Document data quality, adoption, security, service and scale findings
- Convert evidence into a phased production scope and accountable value plan
07 · Governance and scale
Operate the solution safely and sustainably
Identity and access
Server-enforced identity, least-privilege app membership, role-specific actions and auditable administration.
Data governance
Source lineage, quality thresholds, update cadence, retention, consent and controlled data sharing.
Operating governance
Named process owners, exception SLAs, escalation, change control and adoption measurement.
Value governance
Baseline, intervention and outcome metrics reviewed on an agreed cadence with accountable owners.
Your outcome, powered by Mappls
Put ADAS, HD Maps & Road Safety Intelligence into your operating context
Bring the baseline, workflow, users, data and success criteria. Mappls will shape a bounded proof and production path.