Route-Based Sensor-Aware Speed Planning and Support for Safety Validation for Level-3 Automated Driving Mercedes-Benz Group AG

Format:

Book

Conference/Event

Author/Creator:

Kohler, Paul Leonhard, author.

Resch, Michael, author.

Conference Name:

2026 Stuttgart International Symposium (2026-07-08 : Stuttgart, Germany)

Language:

English

Subjects (All):

Automated driving systems.

Level 4 (High driving automation).

Level 5 (Full driving automation).

Level 3 (Conditional driving automation).

Visibility.

Sensors and actuators.

Safety testing and procedures.

Local Subjects:

Automated driving systems.

Level 4 (High driving automation).

Level 5 (Full driving automation).

Level 3 (Conditional driving automation).

Visibility.

Sensors and actuators.

Safety testing and procedures.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

Level-3 and higher automated driving systems require longitudinal speed strategies that remain consistent with both physical stopping feasibility and realistic sensing constraints. This paper presents a route-based, sensor-aware speed planning method that supports safety validation and explicitly couples longitudinal driving strategy with sensor field-of-view coverage. Based on a concrete route extracted from digital maps and enriched with fleet data, point-wise maximum speeds are computed considering road curvature, speed limits, and comfort constraints. From the resulting drivable speed profile, physically consistent stopping paths and their endpoints are calculated for each route position, accounting for friction limits, scenario-dependent deceleration capabilities, and system delays between perception and braking. The set of stopping paths is aggregated into a region of interest (ROI) representing the spatial area that must be reliably perceived to guarantee safe stopping. This ROI is overlaid with the geometric fields of view of camera, radar, and lidar sensors, enabling the definition of a compact and interpretable key performance indicator (KPI) based on the number of sensor modalities covering critical regions. Rather than evaluating a specific sensor configuration, the proposed KPI establishes a geometric interface between braking-based perception requirements and multi-modal sensing coverage. The approach reveals the structural sensitivity of perception demands to route geometry and braking assumptions and provides a systematic basis for perception-aware speed release decisions. The method is applicable to highways, interchanges, and other route types, and contributes a modular geometric framework for sensor-aware safety analysis in Level-3 and higher automated driving systems

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license