Spatiotemporal Tube-Based Safety-Certificate for Autonomous Navigation of Articulated Vehicles

Mohd. Faizuddin Faruqui; Ratnangshu Das; Ravi Kumar L; Pushpak Jagtap

cs.ROAug 14, 2026

Spatiotemporal Tube-Based Safety-Certificate for Autonomous Navigation of Articulated Vehicles

Authors: Mohd. Faizuddin Faruqui, Ratnangshu Das, Ravi Kumar L, Pushpak Jagtap

Organizations: Department of Cyber-Physical Systems, Indian Institute of Science, Bengaluru, India · U.R. Rao Satellite Centre, ISRO, Bangalore, India · Department of Aerospace Engineering, Indian Institute of Science, Bengaluru, India

Abstract

Articulated vehicles are the workhorses of freight transportation, and their autonomous navigation is challenging. Their physical characteristics and motion constraints pose significant challenges in manoeuvring these vehicles on narrow routes. This paper presents a spatiotemporal tube-based approach to plan autonomous navigation of vehicles like tractor semi-trailers, truck/ tractor trailers, towing Automated Guided Vehicles (AGVs), and road trains. This planning approach provides a certified path plan for the truck or tractor, ensuring that the towed series of trailers always remains within the road corridor, limited by permissible corrections. The planning leverages the kinematics of the linked elements along with sway constraints to arrive at a safe tube for the actuated prime mover. We modify the spatiotemporal tube using permissible corrections to provide a route safety certificate to the vehicle for the given route. The proposed planning method is verified on a truck-trailer navigation simulation for a complex route.

Figures & tables

Fig. 1 : Kinematic geometry for an instant of prime mover position

Fig. 2 : STETs for ASNav task in scenario-5 (a) Prime mover tube, (b)-(d) Trailer-1 to Trailer-3 tubes (color-map along left and route boundary of trailer-3 represents boundary leeway)

Panel of Figure 3 — Fig. 3 : Simulation results (route length 543.620 m): (a) Left leeway $\eta_{l}$ for last trailer in scenario-5, (b) Right leeway $\eta_{r}$ for last trailer scenario-5, (c) RSC for scenarios 1-7 (S:1 to S:7) against $w_{min}$

Sc. No.	$N+1$	$L$ (m)	$\pm\theta$ (deg.)	$\mathcal{D}$ (m $\times$ m)	$h_{\Omega}$	$h_{\Xi}$	$w_{\min}$ (m)
1	3	4.67	9.4	11.2 $\times$ 2.6	0.231	1.003	7.683
2	6	3.54	11.0	11.3 $\times$ 2.8	0.636	1.004	5.572
3	6	3.97	10.7	10.1 $\times$ 2.7	0.335	1.020	8.668
4	4	3.50	5.9	12.2 $\times$ 2.8	0.351	1.001	4.535
5	4	4.19	12.4	10.1 $\times$ 2.5	0.250	1.006	7.984
6	6	4.91	5.4	10.9 $\times$ 2.7	0.534	1.005	5.059

TABLE I : Route and vehicle configuration scenarios

Feature	Proposed- RSC corridor certification	Optimization-based approach and efficient planner [ 21 ] , [ 22 ]	Robust planning [ 23 ]
Guarantee tool	Route Safety Certificate (RSC) that decides for a vehicle corridor safety	Feasibility based safety: Sequential Quadratic Programming applied to a non-linear optimal control problem	Robust Control Invariant (RCI) tubes and Control Contraction Metrics (CCM)
System knowledge	Relies on kinematic bounds, hence suited for low slip regimes	Detailed knowledge of vehicle kinematics and route details required	Detailed non-linear dynamics of the system and disturbance characterization required
Computational Efficiency	No heavy optimization-based calculations required	Computationally heavy due to trajectory optimization/ search problem	Performs convex optimization, hence computationally expensive
Scalability	Calculation is done using STET, hence linear scaling with trailers	Trailer count increases dimensionality, more iterations to achieve feasible solutions	Polynomial/ exponential scaling with increase in states or degree of polynomials

TABLE II : Comparison of safety assurance mechanisms for articulated vehicles within a given route corridor

Autonomous Driving Planning

Autonomous driving planning selects safe, feasible vehicle actions and trajectories amid traffic, uncertainty, road rules, and changing goals.

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Aug 8, 2026J/K move · Enter open · S save

Scalable High-Speed Lateral Control for Single-Body and Articulated Autonomous Vehicles

Aashish Shaju, Steve Southward, Mehdi Ahmadian

Center for Vehicle Systems and Safety (CVeSS), Virginia Tech, Blacksburg, VA 24060, USA

Robotic Control Autonomous Driving Adaptive Control

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This paper presents a scalable lateral control framework for robust path tracking of single-body and articulated autonomous vehicles at high speeds. A clothoid-based controller is extended with three key adaptations: 1) an integrated tangential check and Frechet distance method for optimized lookahead and oscillation mitigation; 2) real-time trajectory segment classification for dynamic adjustment of the lookahead search range; and 3) a dual-adaptive, rate-controlled lookahead mechanism responsive to cross-track error and upcoming path geometry. To support different vehicle configurations, the framework also incorporates flexible tracking point selection and curvature-to-steering lookup tables, enabling control of points such as the tractor rear axle, hitch point, or trailer-related locations without changing the core control architecture. The controller is evaluated using high-fidelity TruckSim and Simulink simulations for both standalone and articulated vehicles across dual lane changes and winding-road scenarios. Results demonstrate stable high-speed path tracking, reduced oscillations, effective lookahead adaptation, low cross-track errors, and acceptable lateral acceleration across different vehicle configurations. The results support the use of a unified lateral control architecture that can scale from single-body to articulated autonomous vehicles.

Abstract

Autonomous Driving Planning

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