Predictive Braking on a Nonplanar Road

TL;DR

The paper tackles the safety challenge posed by nonplanar road geometry, where adhesion, load transfer, and rollover risks alter operating limits. It introduces a general nonplanar road model and a convex predictive braking framework that enforces friction cone constraints, road-contact loads, and velocity continuity on a parametric surface $x^p(s,y)$, with planning expressed through stage-based decisions and a finite-horizon optimization. Key contributions include a novel nonplanar safety constraint framework, a convex optimization formulation for predictive braking with accelerometer-informed EBD, and a demonstration on a simulated off-camber turn showing improved safety for both human-driven and autonomous operation. The approach enables safer operation on complex road geometries and is extensible to active steering, suspension, and powertrain control, enhancing ADAS and autonomy in realistic driving environments.

Abstract

We present an approach for predictive braking of a four-wheeled vehicle on a nonplanar road. Our main contribution is a methodology to consider friction and road contact safety on general smooth road geometry. We use this to develop an active safety system to preemptively reduce vehicle speed for upcoming road geometry, such as off-camber turns. Our system may be used for human-driven or autonomous vehicles and we demonstrate it with a simulated ADAS scenario. We show that loss of control due to driver error on nonplanar roads can be mitigated by our approach.

Figures (3)

• Figure 1: Parametric road surface $\boldsymbol{x}^p$. Coordinates $s$, $y$, and $\theta^s$ describe vehicle pose relative to the surface.
• Figure 2: Wheelbase dimensions used for weight distribution.
• Figure 3: Vehicle trajectories on nonplanar u-turn, starting from bottom left